Reduction of data stored on a block processing storage system

ABSTRACT

Techniques and systems for reducing data stored on a block processing storage system are described. A losslessly reduced representation of a data block can include references to one or more prime data element blocks, and optionally a description of a reconstitution program which, when applied to the one or more prime data element blocks, results in the data block.

RELATED APPLICATIONS

This patent application is a continuation of, and claims priority to,pending U.S. patent application Ser. No. 16/133,497 filed on 17 Sep.2018, the contents of which are herein incorporated by reference intheir entirety for all purposes. U.S. patent application Ser. No.16/133,497 is a continuation of, and claims priority to, U.S. patentapplication Ser. No. 14/757,929 (U.S. Pat. No. 10,108,633) filed on 23Dec. 2015, the contents of which are herein incorporated by reference intheir entirety for all purposes. U.S. patent application Ser. No.14/757,929 (U.S. Pat. No. 10,108,633) claims priority to the followingU.S. Provisional Applications, the contents of which are hereinincorporated by reference in their entirety for all purposes:

-   -   1. U.S. Provisional Application Ser. No. 62/097,070, filed on 27        Dec. 2014;    -   2. U.S. Provisional Application Ser. No. 62/175,444, filed on 15        Jun. 2015;    -   3. U.S. Provisional Application Ser. No. 62/187,814, filed on 2        Jul. 2015;    -   4. U.S. Provisional Application Ser. No. 62/194,240, filed on 19        Jul. 2015;    -   5. U.S. Provisional Application Ser. No. 62/265,981, filed on 10        Dec. 2015;    -   6. U.S. Provisional Application Ser. No. 62/268,496, filed on 16        Dec. 2015; and    -   7. U.S. Provisional Application Ser. No. 62/268,503, filed on 17        Dec. 2015.

The subject matter of this application is related to the subject matterin the following issued U.S. patents by the same inventor:

-   -   1. U.S. application Ser. No. 14/685,199, filed on 13 Apr. 2015,        which issued as U.S. Pat. No. 9,286,313, which is herein        incorporated by reference in its entirety for all purposes;    -   2. U.S. application Ser. No. 14/685,191, filed on 13 Apr. 2015,        which issued as U.S. Pat. No. 9,292,584, which is herein        incorporated by reference in its entirety for all purposes;    -   3. U.S. application Ser. No. 14/998,330, filed on 23 Dec. 2015,        which issued as U.S. Pat. No. 9,582,514, which is herein        incorporated by reference in its entirety for all purposes; and    -   4. U.S. application Ser. No. 14/757,956, filed on 23 Dec. 2015,        which issued as U.S. Pat. No. 9,594,765, which is herein        incorporated by reference in its entirety for all purposes.

BACKGROUND Technical Field

This disclosure generally relates to data storage, search, retrieval,and communication. More specifically, this disclosure relates toperforming multidimensional search and content-associative retrieval ondata that has been losslessly reduced using a prime data sieve.

Related Art

The modern information age is marked by the creation, capture, andanalysis of enormous amounts of data. New data is generated from diversesources, examples of which include purchase transaction records,corporate and government records and communications, email, social mediaposts, digital pictures and videos, machine logs, signals from embeddeddevices, digital sensors, cellular phone global positioning satellites,space satellites, scientific computing, and the grand challengesciences. Data is generated in diverse formats, and much of it isunstructured and unsuited for entry into traditional databases.Businesses, governments, and individuals generate data at anunprecedented rate and struggle to store, analyze, and communicate thisdata. Tens of billions of dollars are spent annually on purchases ofstorage systems to hold the accumulating data. Similarly large amountsare spent on computer systems to process the data.

In most modern computer and storage systems, data is accommodated anddeployed across multiple tiers of storage, organized as a storagehierarchy. The data that is needed to be accessed often and quickly isplaced in the fastest albeit most expensive tier, while the bulk of thedata (including copies for backup) is preferably stored in the densestand cheapest storage medium. The fastest and most expensive tier of datastorage is the computer system's volatile random access memory or RAM,residing in close proximity to the microprocessor core, and offering thelowest latency and the highest bandwidth for random access of data.Progressively denser and cheaper but slower tiers (with progressivelyhigher latency and lower bandwidth of random access) includenon-volatile solid state memory or flash storage, hard disk drives(HDDs), and finally tape drives.

In order to more effectively store and process the growing data, thecomputer industry continues to make improvements to the density andspeed of the data storage medium and to the processing power ofcomputers. However, the increase in the volume of data far outstrips theimprovement in capacity and density of the computing and data storagesystems. Statistics from the data storage industry in 2014 reveal thatnew data created and captured in the past couple of years comprises amajority of the data ever captured in the world. The amount of datacreated in the world to date is estimated to exceed multiple zettabytes(a zettabyte is 10²¹ bytes). The massive increase in the data placesgreat demands on data storage, computing, and communication systems thatmust store, process, and communicate this data reliably. This motivatesthe increased use of lossless data reduction or compression techniquesto compact the data so that it can be stored at reduced cost, andlikewise processed and communicated efficiently.

A variety of lossless data reduction or compression techniques haveemerged and evolved over the years. These techniques examine the data tolook for some form of redundancy in the data and exploit that redundancyto realize a reduction of the data footprint without any loss ofinformation. For a given technique that looks to exploit a specific formof redundancy in the data, the degree of data reduction achieved dependsupon how frequently that specific form of redundancy is found in thedata. It is desirable that a data reduction technique be able toflexibly discover and exploit any available redundancy in the data.Since data originates from a wide variety of sources and environmentsand in a variety of formats, there is great interest in the developmentand adoption of universal lossless data reduction techniques to handlethis diverse data. A universal data reduction technique is one whichrequires no prior knowledge of the input data other than the alphabet;hence, it can be applied generally to any and all data without needingto know beforehand the structure and statistical distributioncharacteristics of the data.

Goodness metrics that can be used to compare different implementationsof data compression techniques include the degree of data reductionachieved on the target datasets, the efficiency with which thecompression or reduction is achieved, and the efficiency with which thedata is decompressed and retrieved for further use. The efficiencymetrics assess the performance and cost-effectiveness of the solution.Performance metrics include the throughput or ingest rate at which newdata can be consumed and reduced, the latency or time required to reducethe input data, the throughput or rate at which the data can bedecompressed and retrieved, and the latency or time required todecompress and retrieve the data. Cost metrics include the cost of anydedicated hardware components required, such as the microprocessor coresor the microprocessor utilization (central processing unit utilization),the amount of dedicated scratch memory and memory bandwidth, as well asthe number of accesses and bandwidth required from the various tiers ofstorage that hold the data. Note that reducing the footprint of the datawhile simultaneously providing efficient and speedy compression as wellas decompression and retrieval has the benefit not only of reducing theoverall cost to store and communicate the data but also of efficientlyenabling subsequent processing of the data.

Many of the universal data compression techniques currently being usedin the industry derive from the Lempel-Ziv compression method developedin 1977 by Abraham Lempel and Jacob Ziv—see e.g., Jacob Ziv and AbrahamLempel, “A Universal Algorithm for Sequential Data Compression,” IEEEtransactions on information theory, Vol. IT-23, No. 3, May 1977. Thismethod became the basis for enabling efficient data transmission via theInternet. The Lempel-Ziv methods (named LZ77, LZ78 and their variants)reduce the data footprint by replacing repeated occurrences of a stringwith a reference to a previous occurrence seen within a sliding windowof a sequentially presented input data stream. On consuming a freshstring from a given block of data from the input data stream, thesetechniques search through all strings previously seen within the currentand previous blocks up to the length of the window. If the fresh stringis a duplicate, it is replaced by a backward reference to the originalstring. If the number of bytes eliminated by the duplicate string islarger than the number of bytes required for the backward reference, areduction of the data has been achieved. To search through all stringsseen in the window, and to provide maximal string matching,implementations of these techniques employ a variety of schemes,including iterative scanning and building a temporary bookkeepingstructure that contains a dictionary of all the strings seen in thewindow. Upon consuming new bytes of input to assemble a fresh string,these techniques either scan through all the bytes in the existingwindow, or make references to the dictionary of strings (followed bysome computation) to decide whether a duplicate has been found and toreplace it with a backward reference (or, alternatively, to decidewhether an addition needs to be made to the dictionary).

The Lempel-Ziv compression method is often accompanied by a secondoptimization applied to the data, in which source symbols aredynamically re-encoded based upon their frequency or probability ofoccurrence in the data block being compressed, often employing avariable-width encoding scheme so that shorter length codes are used forthe more frequent symbols, thus leading to a reduction of the data. Forexample, see David A. Huffman, “A Method for the Construction ofMinimum-Redundancy Codes,” Proceedings of the IRE—Institute of RadioEngineers, September 1952, pp. 1098-1101. This technique is referred toas Huffman re-encoding, and typically needs a first pass through thedata to compute the frequencies and a second pass to actually encode thedata. Several variations along this theme are also in use.

One example that uses these techniques is a scheme known as “Deflate”which combines the Lempel-Ziv LZ77 compression method with Huffmanre-encoding. Deflate provides a compressed stream data formatspecification that specifies a method for representing a sequence ofbytes as a (usually shorter) sequence of bits, and a method for packingthe latter bit sequences into bytes. The Deflate scheme was originallydesigned by Phillip W. Katz of PKWARE, Inc. for the PKZIP archivingutility. See e.g., “String searcher, and compressor using same,” PhillipW. Katz, U.S. Pat. No. 5,051,745, Sep. 24, 1991. U.S. Pat. No. 5,051,745describes a method for searching a vector of symbols (the window) for apredetermined target string (the input string). The solution employs apointer array with a pointer to each of the symbols in the window, anduses a method of hashing to filter the possible locations in the windowthat are required to be searched for an identical copy of the inputstring. This is followed by scanning and string matching at thoselocations.

The Deflate scheme is implemented in the zlib library for datacompression. Zlib is a software library that is a key component ofseveral software platforms such as Linux, Mac OS X, iOS, and a varietyof gaming consoles. The zlib library provides Deflate compression anddecompression code for use by zip (file archiving), gzip (single filecompression), png (Portable Network Graphics format for losslesslycompressed images), and many other applications. Zlib is now widely usedfor data transmission and storage. Most HTTP transactions by servers andbrowsers compress and decompress the data using zlib. Similarimplementations are increasingly being used by data storage systems.

A paper entitled “High Performance ZLIB Compression on Intel®Architecture Processors,” that was published by Intel Corp. in April2014 characterizes the compression and performance of an optimizedversion of the zlib library running on a contemporary Intel processor(Core I7 4770 processor, 3.4 GHz, 8 MB cache) and operating upon theCalgary corpus of data. The Deflate format used in zlib sets the minimumstring length for matching to be 3 characters, the maximum length of thematch to be 256 characters, and the size of the window to be 32kilobytes. The implementation provides controls for 9 levels ofoptimization, with level 9 providing the highest compression but usingthe most computation and performing the most exhaustive matching ofstrings, and level 1 being the fastest level and employing greedy stringmatching. The paper reports a compression ratio of 51% using the zliblevel 1 (fastest level) using a single-threaded processor and spendingan average of 17.66 clocks/byte of input data. At a clock frequency of3.4 GHz, this implies an ingest rate of 192 MB/sec while using up asingle microprocessor core. The report also describes how theperformance rapidly drops to an ingest rate of 38 MB/sec (average of88.1 clocks/byte) using optimization level 6 for a modest gain incompression, and to an ingest rate of 16 MB/sec (average of 209.5clocks/byte) using optimization level 9.

Existing data compression solutions typically operate at ingest ratesranging from 10 MB/sec to 200 MB/sec using a single processor core oncontemporary microprocessors. To further boost the ingest rate, multiplecores are employed, or the window size is reduced. Even furtherimprovements to the ingest rate are achieved using custom hardwareaccelerators, albeit at increased cost.

Existing data compression methods described above are effective atexploiting fine-grained redundancy at the level of short strings andsymbols in a local window typically the size of a single message or fileor perhaps a few files. These methods have serious limitations anddrawbacks when they are used in applications that operate on large orextremely large datasets and that require high rates of data ingestionand data retrieval.

One important limitation is that practical implementations of thesemethods can exploit redundancy efficiently only within a local window.While these implementations can accept arbitrarily long input streams ofdata, efficiency dictates that a limit be placed on the size of thewindow across which fine-grained redundancy is to be discovered. Thesemethods are highly compute-intensive and need frequent and speedy accessto all the data in the window. String matching and lookups of thevarious bookkeeping structures are triggered upon consuming each freshbyte (or few bytes) of input data that creates a fresh input string. Inorder to achieve desired ingest rates, the window and associatedmachinery for string matching must reside mostly in the processor cachesubsystem, which in practice places a constraint on the window size.

For example, to achieve an ingest rate of 200 MB/sec on a singleprocessor core, the available time budget on average per ingested byte(inclusive of all data accesses and compute) is 5 ns., which means 17clocks using a contemporary processor with operating frequency of 3.4GHz. This budget accommodates accesses to on-chip caches (which take ahandful of cycles) followed by some string matching. Current processorshave on-chip caches of several megabytes of capacity. An access to mainmemory takes over 200 cycles (˜70 ns.), so larger windows residingmostly in memory will further slow the ingest rate. Also, as the windowsize increases, and the distance to a duplicate string increases, sodoes the cost to specify the length of backward references, thusencouraging only longer strings to be searched across the wider scopefor duplication.

On most contemporary data storage systems, the footprint of the datastored across the various tiers of the storage hierarchy is severalorders of magnitude larger than the memory capacity in the system. Forexample, while a system could provide hundreds of gigabytes of memory,the data footprint of the active data residing in flash storage could bein the tens of terabytes, and the total data in the storage system couldbe in the range of hundreds of terabytes to multiple petabytes. Also,the achievable throughput of data accesses to subsequent tiers ofstorage drops by an order of magnitude or more for each successive tier.When the sliding window gets so large that it can no longer fit inmemory, these techniques get throttled by the significantly lowerbandwidth and higher latency of random IO (Input or Output operations)access to the next levels of data storage.

For example, consider a file or a page of 4 kilobytes of incoming datathat can be assembled from existing data by making references to, say,100 strings of average length of 40 bytes that already exist in the dataand are spread across a 256 terabyte footprint. Each reference wouldcost 6 bytes to specify its address and 1 byte for string length whilepromising to save 40 bytes. Although the page described in this examplecan be compressed by more than fivefold, the ingest rate for this pagewould be limited by the 100 or more IO accesses to the storage systemneeded to fetch and verify the 100 duplicate strings (even if one couldperfectly and cheaply predict where these strings reside). A storagesystem that offers 250,000 random IO accesses/sec (which means bandwidthof 1 GB/sec of random accesses to pages of 4 KB) could compress only2,500 such pages of 4 KB size per second for an ingest rate of a mere 10MB/sec while using up all the bandwidth of the storage system, renderingit unavailable as a storage system.

Implementations of conventional compression methods with large windowsizes of the order of terabytes or petabytes will be starved by thereduced bandwidth of data access to the storage system, and would beunacceptably slow. Hence, practical implementations of these techniquesefficiently discover and exploit redundancy only if it exists locally,on window sizes that fit in the processor cache or system memory. Ifredundant data is separated either spatially or temporally from incomingdata by multiple terabytes, petabytes, or exabytes, theseimplementations will be unable to discover the redundancy at acceptablespeeds, being limited by storage access bandwidth.

Another limitation of conventional methods is that they are not suitedfor random access of data. Blocks of data spanning the entire windowthat was compressed need to be decompressed before any chunk within anyblock can be accessed. This places a practical limit on the size of thewindow. Additionally, operations (e.g., a search operation) that aretraditionally performed on uncompressed data cannot be efficientlyperformed on the compressed data.

Yet another limitation of conventional methods (and, in particular,Lempel-Ziv based methods) is that they search for redundancy only alongone dimension—that of replacing identical strings by backwardreferences. A limitation of the Huffman re-encoding scheme is that itneeds two passes through the data, to calculate frequencies and thenre-encode. This becomes slow on larger blocks.

Data compression methods that detect long duplicate strings across aglobal store of data often use a combination of digital fingerprintingand hashing schemes. This compression process is referred to as datadeduplication. The most basic technique of data deduplication breaks upfiles into fixed-sized blocks and looks for duplicate blocks across thedata repository. If a copy of a file is created, each block in the firstfile will have a duplicate in the second file and the duplicate can bereplaced with a reference to the original block. To speed up matching ofpotentially duplicate blocks, a method of hashing is employed. A hashfunction is a function that converts a string into a numeric value,called its hash value. If two strings are equal, their hash values arealso equal. Hash functions map multiple strings to a given hash value,whereby long strings can be reduced to a hash value of much shorterlength. Matching of the hash values will be much faster than matching oftwo long strings; hence, matching of the hash values is done first, tofilter possible strings that might be duplicates. If the hash value ofthe input string or block matches a hash value of strings or blocks thatexist in the repository, the input string can then be compared with eachstring in the repository that has the same hash value to confirm theexistence of the duplicate.

Breaking up a file into fixed-sized blocks is simple and convenient, andfixed-sized blocks are highly desirable in a high-performance storagesystem. However, this technique has limitations in the amount ofredundancy it can uncover, which means that these techniques have lowlevels of compression. For example, if a copy of a first file is made tocreate a second file, and if even a single byte of data is inserted intothe second file, the alignment of all downstream blocks will change, thehash value of each new block will be computed afresh, and the datadeduplication method will no longer find all the duplicates.

To address this limitation in data deduplication methods, the industryhas adopted the use of fingerprinting to synchronize and align datastreams at locations of matching content. This latter scheme leads tovariable-sized blocks based on the fingerprints. Michael Rabin showedhow randomly chosen irreducible polynomials can be used to fingerprint abit-string—see e.g., Michael O. Rabin, “Fingerprinting by RandomPolynomials,” Center for Research in Computing Technology, HarvardUniversity, TR-15-81, 1981. In this scheme, a randomly chosen primenumber p is used to fingerprint a long character-string by computing theresidue of that string viewed as a large integer modulo p. This schemerequires performing integer arithmetic on k-bit integers, wherek=log₂(p). Alternatively, a random irreducible prime polynomial of orderk can be used, and the fingerprint is then the polynomial representationof the data modulo the prime polynomial.

This method of fingerprinting is used in data deduplication systems toidentify suitable locations at which to establish chunk boundaries, sothat the system can look for duplicates of these chunks in a globalrepository. Chunk boundaries can be set upon finding fingerprints ofspecific values. As an example of such usage, a fingerprint can becalculated for each and every 48-byte string in the input data (startingat the first byte of the input and then at every successive bytethereafter), by employing a polynomial of order 32 or lower. One canthen examine the lower 13 bits of the 32-bit fingerprint, and set abreakpoint whenever the value of those 13 bits is a pre-specified value(e.g., the value 1). For random data, the likelihood of the 13 bitshaving that particular value would be 1 in 2¹³, so that such abreakpoint is likely to be encountered approximately once every 8 KB,leading to variable-sized chunks of average size 8 KB. The breakpointsor chunk boundaries will effectively be aligned to fingerprints thatdepend upon the content of the data. When no fingerprint is found for along stretch, a breakpoint can be forced at some pre-specifiedthreshold, so that the system is certain to create chunks that areshorter than a pre-specified size for the repository. See e.g., AthichaMuthitacharoen, Benjie Chen, and David Mazières, “A Low-bandwidthNetwork File System,” SOSP '01, Proceedings of the eighteenth ACMsymposium on Operating Systems Principles, Oct. 21, 2001, pp. 174-187.

The Rabin-Karp string matching technique developed by Michael Rabin andRichard Karp provided further improvements to the efficiency offingerprinting and string matching (see e.g., Michael O. Rabin and R.Karp, “Efficient Randomized Pattern-Matching Algorithms,” IBM Jour. ofRes. and Dev., vol. 31, 1987, pp. 249-260). Note that a fingerprintingmethod that examines an m byte substring for its fingerprint canevaluate the fingerprinting polynomial function in O(m) time. Since thismethod would need to be applied on the substring starting at every byteof the, say, n byte input stream, the total effort required to performfingerprinting on the entire data stream would be O(n×m). Rabin-Karpidentified a hash function referred to as a Rolling Hash on which it ispossible to compute the hash value of the next substring from theprevious one by doing only a constant number of operations,independently of the length of the substring. Hence, after shifting onebyte to the right, the computation of the fingerprint on the new m bytestring can be done incrementally. This reduces the effort to compute thefingerprint to O(1), and the total effort for fingerprinting the entiredata stream to O(n), linear with the size of the data. This greatlyspeeds up computation and identification of the fingerprints.

Typical data access and computational requirements for theabove-described data deduplication methods can be described as follows.For a given input, once fingerprinting is completed to create a chunk,and after the hash value for the chunk is computed, these methods firstneed one set of accesses to memory and subsequent tiers of storage tosearch and look up the global hash table that keeps the hash values ofall chunks in the repository. This would typically need a first IOaccess to storage. Upon a match in the hash table, this is followed by asecond set of storage IOs (typically one, but could be more than onedepending upon how many chunks with the same hash value exist in therepository) to fetch the actual data chunks bearing the same hash value.Lastly, byte-by-byte matching is performed to compare the input chunk tothe fetched potentially matching chunks to confirm and identify theduplicate. This is followed by a third storage IO access (to themetadata space) for replacing the new duplicate block with a referenceto the original. If there is no match in the global hash table (or if noduplicate is found), the system needs one IO to enter the new block intothe repository and another IO to update the global hash table to enterin the new hash value. Thus, for large datasets (where the metadata andglobal hash table do not fit in memory, and hence need a storage IO toaccess them) such systems could need an average of three IOs per inputchunk. Further improvements are possible by employing a variety offilters so that misses in the global hash table can often be detectedwithout requiring the first storage IO to access the global hash table,thus reducing the number of IOs needed to process some of the chunksdown to two.

A storage system that offers 250,000 random IO accesses/sec (which meansbandwidth of 1 GB/sec of random accesses to pages of 4 KB) could ingestand deduplicate about 83,333 (250,000 divided by 3 IOs per input chunk)input chunks of average size 4 KB per second, enabling an ingest rate of333 MB/sec while using up all the bandwidth of the storage system. Ifonly half of the bandwidth of the storage system is used (so that theother half is available for accesses to the stored data), such adeduplication system could still deliver ingest rates of 166 MB/sec.These ingest rates (which are limited by I/O bandwidth) are achievableprovided that sufficient processing power is available in the system.Thus, given sufficient processing power, data deduplication systems areable to find large duplicates of data across the global scope of thedata with an economy of IOs and deliver data reduction at ingest ratesin the hundreds of megabytes per second on contemporary storage systems.

Based on the above description, it should be clear that, while thesededuplication methods are effective at finding duplicates of longstrings across a global scope, they are effective mainly at findinglarge duplicates. If there are variations or modifications to the dataat a finer grain, the available redundancy will not be found using thismethod. This greatly reduces the breadth of datasets across which thesemethods are useful. These methods have found use in certain data storagesystems and applications, e.g., regular backup of data, where the newdata being backed up has only a few files modified and the rest are allduplicates of the files that were saved in the previous backup.Likewise, data deduplication based systems are often deployed inenvironments where multiple exact copies of the data or code are made,such as in virtualized environments in datacenters. However, as dataevolves and is modified more generally or at a finer grain, datadeduplication based techniques lose their effectiveness.

Some approaches (usually employed in data backup applications) do notperform the actual byte-by-byte comparison between the input data andthe string whose hash value matches that of the input. Such solutionsrely on the low probability of a collision using strong hash functionslike the SHA-1. However, due to the finite non-zero probability of acollision (where multiple different strings could map to the same hashvalue), such methods cannot be considered to provide lossless datareduction, and would not, therefore, meet the high data-integrityrequirements of primary storage and communication.

Some approaches combine multiple existing data compression techniques.Typically, in such a setup, the global data deduplication methods areapplied to the data first. Subsequently, on the deduplicated dataset,and employing a small window, the Lempel-Ziv string compression methodscombined with Huffman re-encoding are applied to achieve further datareduction.

However, in spite of employing all hitherto-known techniques, therecontinues to be a gap of several orders of magnitude between the needsof the growing and accumulating data and what the world economy canaffordably accommodate using the best available modern storage systems.Given the extraordinary requirements of storage capacity demanded by thegrowing data, there continues to be a need for improved ways to furtherreduce the footprint of the data. There continues to be a need todevelop methods that address the limitations of existing techniques, orthat exploit available redundancy in the data along dimensions that havenot been addressed by existing techniques. At the same time, itcontinues to be important to be able to efficiently access and retrievethe data at an acceptable speed and at an acceptable cost of processing.

In summary, there continues to be a long-felt need for lossless datareduction solutions that can exploit redundancy across large andextremely large datasets and provide high rates of data ingestion anddata retrieval.

SUMMARY

Embodiments described herein feature techniques and systems forlosslessly reducing data in a block processing storage system thatstores data blocks. Specifically, a new data block associated with alogical block address may be losslessly reduced by (1) identifying oneor more prime data element blocks by using the new data block to performa content-associative lookup on a data structure that organizes primedata element blocks based on their contents, and (2) using the one ormore prime data element blocks to losslessly reduce the new data block,thereby obtaining a losslessly reduced representation of the new datablock. The losslessly reduced representation of the new data block maybe stored, and an association between the logical block address and thelosslessly reduced representation of the new data block may be stored.

In embodiments described herein, the data structure may be a tree datastructure, where performing the content-associative lookup may includetraversing a sequence of edges in the tree data structure, where eachedge in the sequence of edges may correspond to a portion of a nameassociated with the new data block, and where each portion of the namethat is used to navigate to a node in the tree data structure maycorrespond to content that is present in all prime data element blocksthat are within a subtree rooted at the node.

Some embodiments described herein may increment aprime-data-element-count field associated with each prime data elementblock referenced by the losslessly reduced representation of the newdata block. In some embodiments described herein, the associationbetween the logical block address and the losslessly reducedrepresentation of the new data block may replace an association betweenthe logical block address and a losslessly reduced representation of anold data block. These embodiments may decrement aprime-data-element-count field associated with each prime data elementblock referenced by the losslessly reduced representation of the olddata block.

In some embodiments described herein, the losslessly reducedrepresentation of the new data block may include a reference to eachprime data element block in the one or more prime data element blocksand a description of a reconstitution program, where the new data blockcan be reconstituted by applying the reconstitution program to the oneor more prime data element blocks. Some embodiments may increment areconstitution-program-count field associated with the reconstitutionprogram. In some embodiments described herein, the association betweenthe logical block address and the losslessly reduced representation ofthe new data block may replace an association between the logical blockaddress and a losslessly reduced representation of an old data block,and wherein the method further comprises decrementing areconstitution-program-count field associated with a reconstitutionprogram referenced by the losslessly reduced representation of the olddata block.

In some embodiments described herein, the description of thereconstitution program specifies a sequence of transformations which,when applied to the one or more prime data element blocks, results inthe new data block.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A illustrates methods and apparatuses for data reduction thatfactorize input data into elements and derive these from Prime DataElements resident in a Prime Data Sieve in accordance with someembodiments described herein.

FIGS. 1B-1G illustrate variations of the methods and apparatusesillustrated in FIG. 1A in accordance with some embodiments describedherein.

FIG. 1H presents an example of a format and a specification describingthe structure of the Distilled Data in accordance with some embodimentsdescribed herein.

FIGS. 1I through 1P illustrate the conceptual transformation of InputData into the losslessly reduced form for the variations of the methodsand apparatuses for data reduction shown in FIG. 1A through FIG. 1G.

FIG. 2 illustrates a process for data reduction by factorizing inputdata into elements and deriving these elements from Prime Data Elementsresiding in a Prime Data Sieve in accordance with some embodimentsdescribed herein.

FIGS. 3A, 3B, 3C, 3D, and 3E illustrate different data organizationsystems that may be used to organize Prime Data Elements based on theirName in accordance with some embodiments described herein.

FIG. 3F presents a self-describing tree node data structure inaccordance with some embodiments described herein.

FIG. 3G presents a self-describing leaf node data structure inaccordance with some embodiments described herein.

FIG. 3H presents a self-describing leaf node data structure thatincludes the Navigation Lookahead field in accordance with someembodiments described herein.

FIG. 4 shows an example of how 256 TB of prime data may be organized intree form, and presents how the tree may be laid out in memory andstorage in accordance with some embodiments described herein.

FIGS. 5A-5C illustrate an actual example of how data can be organizedusing embodiments described herein.

FIGS. 6A-6C show how tree data structures can be used forcontent-associative mappers described in reference to FIGS. 1A-1C,respectively, in accordance with some embodiments described herein.

FIG. 7A provides an example of the transformations that could bespecified in the Reconstitution Program in accordance with someembodiments described herein.

FIG. 7B shows examples of the results of Candidate Elements beingderived from Prime Data Elements in accordance with some embodimentsdescribed herein.

FIGS. 8A-8E illustrate how data reduction can be performed byfactorizing input data into fixed sized elements and organizing theelements in a tree data structure that was described in reference toFIGS. 3D and 3E in accordance with some embodiments described herein.

FIGS. 9A-9C illustrate an example of the Data Distillation™ scheme basedon the system shown in FIG. 1C in accordance with some embodimentsdescribed herein.

FIG. 10A provides an example of how transformations specified in theReconstitution Program are applied to a Prime Data Element to yield aDerivative Element in accordance with some embodiments described herein.

FIGS. 10B-10C illustrate data retrieval processes in accordance withsome embodiments described herein.

FIG. 11A-11G illustrate systems that include a Data Distillation™mechanism (which can be implemented using software, hardware, or acombination thereof) in accordance with some embodiments describedherein.

FIG. 11H shows how the Data Distillation™ apparatus may interface with asample general purpose computing platform in accordance with someembodiments described herein.

FIG. 11I illustrates how the Data Distillation™ apparatus may be usedfor data reduction in a block processing storage system.

FIGS. 12A-12B show the use of the Data Distillation™ apparatus for thecommunication of data across a bandwidth constrained communicationmedium in accordance with some embodiments described herein.

FIGS. 12C-12K illustrate the various components of the reduced dataproduced by the Data Distillation™ apparatus for various usage models inaccordance with some embodiments described herein.

FIGS. 12L-P illustrate how the Distillation process can be deployed andexecuted on distributed systems to be able to accommodate significantlylarger datasets at very high ingest rates in accordance with someembodiments described herein.

FIGS. 13-17 illustrate how multidimensional search and data retrievalcan be performed on the reduced data in accordance with some embodimentsdescribed herein.

FIGS. 18A-B show a block diagram for an Encoder and Decoder forcompression and decompression of audio data according to the MPEG 1,Layer 3 Standard (also referred to as MP3).

FIG. 18C shows how the Data Distillation apparatus first shown in FIG.1A can be enhanced to perform data reduction on MP3 data.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the invention, and is provided in the context ofa particular application and its requirements. Various modifications tothe disclosed embodiments will be readily apparent to those skilled inthe art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present invention. Thus, the present invention is notlimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features disclosed herein. In thisdisclosure, when a phrase uses the term “and/or” with a set of entities,the phrase covers all possible combinations of the set of entitiesunless specified otherwise. For example, the phrase “X, Y, and/or Z”covers the following seven combinations: “X only,” “Y only,” “Z only,”“X and Y, but not Z,” “X and Z, but not Y,” “Y and Z, but not X,” and“X, Y, and Z.”

Efficient Lossless Reduction of Data Using a Prime Data Sieve

In some embodiments described herein, data is organized and stored toefficiently uncover and exploit redundancy globally across the entiredataset. An input data stream is broken up into constituent pieces orchunks called elements, and redundancy among the elements is detectedand exploited at a grain finer than the element itself, thus reducingthe overall footprint of stored data. A set of elements called PrimeData Elements are identified and used as common and shared buildingblocks for the dataset, and stored in a structure referred to as thePrime Data Store or Prime Data Sieve. A Prime Data Element is simply asequence of bits, bytes, or digits of a certain size. Prime DataElements can be either fixed-sized or variable-sized, depending upon theimplementation. Other constituent elements of the input data are derivedfrom Prime Data Elements and are referred to as Derivative Elements.Thus, input data is factorized into Prime Data Elements and DerivativeElements.

The Prime Data Sieve orders and organizes the Prime Data Elements sothat the Prime Data Sieve can be searched and accessed in acontent-associative manner. Given some input content, with somerestrictions, the Prime Data Sieve can be queried to retrieve Prime DataElements containing that content. Given an input element, the Prime DataSieve can be searched, using the value of the element, or the values ofcertain fields in the element, to quickly provide either one or a smallset of Prime Data Elements from which the input element can be derivedwith minimal storage required to specify the derivation. In someembodiments, the elements in the Prime Data Sieve are organized in treeform. A Derivative Element is derived from a Prime Data Element byperforming transformations on it, such transformations being specifiedin a Reconstitution Program, which describes how to generate theDerivative Element from one or more Prime Data Elements. A DistanceThreshold specifies a limit on the size of the stored footprint of aDerivative Element. This threshold effectively specifies the maximumallowable distance of Derivative Elements from Prime Data Elements, andalso places a limit on the size of the Reconstitution Program that canbe used to generate a Derivative Element.

Retrieval of derivative data is accomplished by executing theReconstitution Program on the one or more Prime Data Elements specifiedby the derivation.

In this disclosure, the above-described universal lossless datareduction technique may be referred to as a Data Distillation™ process.It performs a function similar to distillation in chemistry—separating amixture into its constituent elements. The Prime Data Sieve is alsoreferred to as the Sieve or the Data Distillation™ Sieve or the PrimeData Store.

In this scheme, the input data stream is factorized into a sequence ofelements, each element being either a Prime Data Element or a DerivativeElement which derives from one or more Prime Data Elements. Each elementis transformed into a losslessly reduced representation which, in thecase of a Prime Data Element includes a reference to the Prime DataElement, and in the case of a Derivative Element includes references tothe one or more Prime Data Elements involved in the derivation, and adescription of the Reconstitution Program. Thus the input data stream isfactorized into a sequence of elements that are in the losslesslyreduced representation. This sequence of elements (appearing in thelosslessly reduced representation) is referred to as a distilled datastream or distilled data. The sequence of elements in the distilled datahas a one-to-one correspondence to the sequence of elements in the inputdata, i.e., the n^(th) element in the sequence of elements in thedistilled data corresponds to the n^(th) element in the sequence ofelements in the input data.

The universal lossless data reduction technique described in thisdisclosure receives an input data stream and converts it into thecombination of a distilled data stream and a Prime Data Sieve, such thatthe sum of the footprints of the distilled data stream and the PrimeData Sieve is usually smaller than the footprint of the input datastream. In this disclosure, the distilled data stream and the Prime DataSieve are collectively called the losslessly reduced data, and will alsobe referred to interchangeably as the “reduced data stream” or “reduceddata” or “Reduced Data” Likewise, for the sequence of elements that isproduced by the lossless data reduction techniques described in thisdisclosure, and that appear in the losslessly reduced format, thefollowing terms are used interchangeably: “reduced output data stream,”“reduced output data”, “distilled data stream,” “distilled data”, and“Distilled Data.”

FIG. 1A illustrates methods and apparatuses for data reduction thatfactorize input data into elements and derive these from Prime DataElements resident in a Prime Data Sieve in accordance with someembodiments described herein. This figure illustrates an overall blockdiagram of the data reduction or Data Distillation™ methods andapparatuses and provides an overview of the functional components,structures, and operations. The components and/or operations illustratedin FIG. 1A may be realized using software, hardware, or a combinationthereof.

A sequence of bytes is received from an input data stream and presentedas Input Data 102 to Data Reduction Apparatus 103, also referred to asthe Data Distillation™ Apparatus. Parser & Factorizer 104 parses theincoming data and breaks it into chunks or candidate elements. TheFactorizer decides where in the input stream to insert breaks to sliceup the stream into candidate elements. Once two consecutive breaks inthe data have been identified, a Candidate Element 105 is created by theParser and Factorizer and presented to Prime Data Sieve 106, alsoreferred to as the Data Distillation™ Sieve.

Data Distillation™ Sieve or Prime Data Sieve 106 contains all the PrimeData Elements (labelled as PDEs in FIG. 1A), and orders and organizesthem based upon their value or content. The Sieve provides support fortwo kinds of access. First, each of the Prime Data Elements can bedirectly accessed via a reference to the location where the Prime DataElement resides in the Sieve. Second, elements can be accessed in acontent-associative manner by using Content-Associative Mapper 121,which could be implemented in software, hardware, or a combinationthereof. This second form of access to the Sieve is an important featurethat is used by the disclosed embodiments either to identify a PrimeData Element that exactly matches a Candidate Element 105, or toidentify Prime Data Elements from which the candidate element can bederived. Specifically, given a candidate element, e.g., CandidateElement 105, the Prime Data Sieve 106 can be searched (based upon thevalue of the Candidate Element 105, or based upon the value of certainfields in the Candidate Element 105), to quickly provide one or a smallset of Prime Data Elements 107 from which the candidate element can bederived with minimal storage needed to specify the derivation.

The Sieve or Prime Data Sieve 106 can be initialized with a set of PrimeData Elements whose values are spread across the data space.Alternatively, the Sieve can start out empty, and Prime Data Elementscan be added to it dynamically as data is ingested, in accordance withthe Data Distillation™ process described herein in reference to FIGS.1A-C and FIG. 2 .

Deriver 110 receives the Candidate Element 105 and the retrieved PrimeData Elements suitable for derivation 107 (which are contentassociatively retrieved from the Prime Data Sieve 106), determineswhether or not the Candidate Element 105 can be derived from one or moreof these Prime Data Elements, generates Reduced Data Components 115(comprised of references to the relevant Prime Data Elements and theReconstitution Program), and provides updates 114 to the Prime DataSieve. If the candidate element is a duplicate of a retrieved Prime DataElement, the Deriver places into the Distilled Data 108 a reference (orpointer) to the Prime Data Element located in the Prime Data Sieve, andalso an indicator that this is a Prime Data Element. If no duplicate isfound, the Deriver expresses the candidate element as the result of oneor more transformations performed on one or more retrieved Prime DataElements, where the sequence of transformations is collectively referredto as the Reconstitution Program, e.g., Reconstitution Program 119A.Each derivation may require its own unique program to be constructed bythe Deriver. The Reconstitution Program specifies transformations suchas insertions, deletions, replacements, concatenations, arithmetic, andlogical operations that can be applied to the Prime Data Elements.Provided the footprint of the Derivative Element (calculated as the sizeof the Reconstitution Program plus the size of the references to therequired Prime Data Elements) is within a certain specified DistanceThreshold with respect to the candidate element (to enable datareduction), the candidate element is reformulated as a DerivativeElement and replaced by the combination of the Reconstitution Programand references to the relevant Prime Data Element (or elements)—theseform the Reduced Data Components 115 in this case. If the threshold isexceeded, or if no suitable Prime Data Element was retrieved from thePrime Data Sieve, the Prime Data Sieve may be instructed to install thecandidate as a fresh Prime Data Element. In this case, the Deriverplaces into the distilled data a reference to the newly added Prime DataElement, and also an indicator that this is a Prime Data Element.

A request for Retrieval of data (e.g., Retrieval Requests 109) can be inthe form of either a reference to a location in the Prime Data Sievecontaining a Prime Data Element, or in the case of a derivative, acombination of such a reference to a Prime Data Element and anassociated Reconstitution Program (or in the case of a derivative basedon multiple Prime Data Elements, a combination of the references tomultiple Prime Data Elements and an associated Reconstitution Program).Using the one or more references to Prime Data Elements in the PrimeData Sieve, Retriever 111 can access the Prime Data Sieve to fetch theone or more Prime Data Elements and provide the one or more Prime DataElements as well as the Reconstitution Program to Reconstitutor 112,which executes the transformations (specified in the ReconstitutionProgram) on the one or more Prime Data Elements to generate theReconstituted Data 116 (which is the data that was requested) anddeliver it to the Retrieved Data Output 113 in response to the dataretrieval request.

In a variation of this embodiment, the Prime Data Elements may be storedin the Sieve in compressed form (using techniques known in the priorart, including Huffman Coding and Lempel Ziv methods) and decompressedwhen needed. This has the advantage of reducing the overall footprint ofthe Prime Data Sieve. The only constraint is that Content AssociativeMapper 121 must continue to provide Content Associative Access to thePrime Data Elements as before.

FIGS. 1B and 1C illustrate variations of the methods and apparatusesillustrated in FIG. 1A in accordance with some embodiments describedherein. In FIG. 1B, Reconstitution Programs may be stored in the PrimeData Sieve and treated like Prime Data Elements. A reference or pointer119B to the Reconstitution Program is provided in Distilled Data 108instead of providing the Reconstitution Program 119A itself. Furtherdata reduction is achieved if the Reconstitution Program is shared byother derivatives, and if the reference or pointer to the ReconstitutionProgram (plus any metadata that is required to distinguish between aReconstitution Program and a reference to a Reconstitution Program)requires less storage space than the Reconstitution Program itself.

In FIG. 1B, Reconstitution Programs may be treated and accessed justlike Prime Data Elements, and, stored in the Prime Data Sieve as PrimeData Elements, thereby allowing content-associative search and retrievalof the Reconstitution Programs from the Prime Data Sieve. During thederivation process to create a Derivative Element, once Deriver 110determines the Reconstitution Program needed for the derivation, it canthen determine whether or not this candidate Reconstitution Program isalready present in the Prime Data Sieve, or whether this candidateReconstitution Program can be derived from another entry that alreadyexists in the Prime Data Sieve. If the candidate Reconstitution Programis already present in the Prime Data Sieve, then Deriver 110 candetermine the reference to the pre-existing entry and include thereference in Distilled Data 108. If the candidate Reconstitution Programcan be derived from an existing entry already resident in the Prime DataSieve, the Deriver can deliver a derivative or reformulation of thecandidate Reconstitution Program to the Distilled Data, i.e., theDeriver places into the Distilled Data a reference to the entry thatpre-exists in the Prime Data Sieve along with an incrementalReconstitution Program that derives the candidate Reconstitution Programfrom the pre-existing entry. If the candidate Reconstitution Program isneither present in the Prime Data Sieve nor derivable from entries inthe Prime Data Sieve, then Deriver 110 can add the ReconstitutionProgram to the Prime Data Sieve (the operation that adds aReconstitution Program to the sieve may return the reference to thenewly added entry), and include the reference to the ReconstitutionProgram in Distilled Data 108.

FIG. 1C presents a variation of the methods and apparatuses illustratedin FIG. 1B in accordance with some embodiments described herein.Specifically, the mechanism in FIG. 1C that is used to store and queryReconstitution Programs is similar to the mechanism that is used tostore and query Prime Data Elements, but the Reconstitution Programs aremaintained in a structure (called the Prime Reconstitution ProgramSieve) separate from that containing the Prime Data Elements. Entries insuch a structure are referred to as Prime Reconstitution Programs(labelled as PRPs in FIG. 1C). Recall that Prime Data Sieve 106 includedcontent-associative mapper 121 that supported fast content-associativelookup operations. The embodiment illustrated in FIG. 1C includesContent-Associative Mapper 122 which is similar to Content-AssociativeMapper 121. In FIG. 1C, Content-Associative Mapper 122 andContent-Associative Mapper 121 have been shown to be part of the PrimeData Sieve or Prime Data Store 106. In other embodiments,content-associative mapper 122 and the Reconstitution Programs may bestored separately from the Prime Data Sieve or Prime Data Store 106 in astructure called the Prime Reconstitution Program Sieve.

In a variation of this embodiment, the Prime Data Elements may be storedin the Sieve in compressed form (using techniques known in the priorart, including Huffman Coding and Lempel Ziv methods) and decompressedwhen needed. Likewise, Prime Reconstitution Programs may be stored inthe Prime Reconstitution Program Sieve in compressed form (usingtechniques known in the prior art, including Huffman Coding and LempelZiv methods) and decompressed when needed. This has the advantage ofreducing the overall footprint of the Prime Data Sieve and PrimeReconstitution Program Sieve. The only constraint is that ContentAssociative Mappers 121 and 122 must continue to provide ContentAssociative Access to the Prime Data Elements and Prime ReconstitutionPrograms as before.

FIG. 1D presents a variation of the methods and apparatuses illustratedin FIG. 1A in accordance with some embodiments described herein.Specifically, in the embodiment described in FIG. 1D, Prime DataElements are stored inline in the Distilled Data. Prime Data Sieve orPrime Data Store 106 continues to provide content-associative access tothe Prime Data Elements, and continues to logically contain the PrimeData Elements. It maintains references or links to the Prime DataElements that are located inline in the Distilled Data. For example, inFIG. 1D, Prime Data Element 130 is located inline in Distilled Data 108.Prime Data Sieve or Prime Data Store 106 maintains a Reference 131 toPrime Data Element 130. Once again, in this setup, the losslesslyreduced representation of a Derivative Element will contain a referenceto the required Prime Data Element. During data retrieval, Retriever 111will fetch the required Prime Data Element from where it is located.

FIG. 1E presents a variation of the methods and apparatuses illustratedin FIG. 1D in accordance with some embodiments described herein.Specifically, in the embodiment described in FIG. 1E, just like in thesetup illustrated in FIG. 1B, Reconstitution Programs may be derivedfrom other Prime Reconstitution Programs, and specified as anIncremental Reconstitution Program plus a reference to a PrimeReconstitution Program. Such Prime Reconstitution Programs are treatedlike Prime Data Elements and logically installed in the Prime DataSieve. Furthermore, in this setup, both Prime Data Elements and PrimeReconstitution Programs are stored inline in the Distilled Data. PrimeData Sieve or Prime Data Store 106 continues to providecontent-associative access to the Prime Data Elements and the PrimeReconstitution Programs, and continues to logically contain these PrimeData Elements and Prime Reconstitution Programs while maintainingreferences or links to where they are located inline in the DistilledData. For example, in FIG. 1E, Prime Data Element 130 is located inlinein Distilled Data 108. Also in FIG. 1E, Prime Reconstitution Program 132is located inline in Distilled Data. Prime Data Sieve or Prime DataStore 106 maintains a Reference 131 (which is Reference_to_PDE_i) toPrime Data Element 130 (which is PDE_i), and a Reference 133 (which isReference_to_PDE_j) to the Prime Reconstitution Program 132 (which isPrime_Recon_Program_l). Once again, in this setup, the losslesslyreduced representation of a Derivative Element will contain a referenceto the required Prime Data Element and required Prime ReconstitutionProgram. During data retrieval, Retriever 111 will fetch the requiredcomponents from where they are located in the corresponding DistilledData.

FIG. 1F presents a variation of the methods and apparatuses illustratedin FIG. 1E in accordance with some embodiments described herein.Specifically, in the embodiment described in FIG. 1F, just like in thesetup illustrated in FIG. 1C, Prime Data Sieve 108 contains separatemappers—Content Associative Mapper 121 for the Prime Data Elements andContent Associative Mapper 122 for the Prime Reconstitution Programs.

FIG. 1G presents a more generalized variation of the methods andapparatuses illustrated in FIG. 1A through FIG. 1F. Specifically, in theembodiment described in FIG. 1G, Prime Data Elements may be locatedeither in the Prime Data Sieve or inline in the Distilled Data. SomePrime Data Elements may be located in the Prime Data Sieve while othersare located inline in the Distilled Data. Likewise, Prime ReconstitutionPrograms may be located either in the Prime Data Sieve or inline in theDistilled Data. Some Prime Reconstitution Programs may be located in thePrime Data Sieve while others are located inline in the Distilled Data.The Prime Data Sieve logically contains all the Prime Data Elements andPrime Reconstitution Programs and in the case where the Prime DataElement or the Prime Reconstitution Program is located inline in theDistilled Data, the Prime Data Sieve furnishes the reference to itslocation.

The foregoing descriptions of methods and apparatuses for data reductionthat factorize input data into elements and derive these from Prime DataElements resident in a Prime Data Sieve have been presented only forpurposes of illustration and description. They are not intended to beexhaustive or to limit the present invention to the forms disclosed.Accordingly, many modifications and variations will be apparent topractitioners skilled in the art.

FIG. 1H presents an example of a format and a specification describingthe structure of the Distilled Data 119A in FIGS. 1A-G of the method andapparatus for the Data Distillation™ process in accordance with someembodiments described herein. Since the Data Distillation™ processfactorizes input data into Prime Data Elements and Derivative Elements,the format for the losslessly reduced representation of the dataidentifies these elements and describes the various components of theseelements in the Distilled Data. The self-describing format identifieseach Element in the Distilled Data, indicates whether it is a Prime DataElement or a Derivative Element, and describes the various components ofthe Element, namely, references to one or more Prime Data Elementsinstalled in the Sieve, a reference to a Reconstitution Programinstalled in the Prime Data Sieve (as in 119B of FIG. 1B) or a referenceto a Reconstitution Program stored in a Prime Reconstitution Program(PRP) Sieve (as in 119C of FIG. 1C), and in-lined ReconstitutionPrograms (RPs). The Prime Reconstitution Program (PRP) Sieve is alsoreferred to interchangeably as a Prime Reconstitution Program (PRP)Store. The format in FIG. 1H has provisions to specify a derivation byexecuting a Reconstitution Program on multiple Prime Data Elements, withthe sizes of the Derivative Element and each of the Prime Data Elementsbeing independently specifiable. The format in FIG. 1H also hasprovision to specify a Prime Data Element which is located inline in theDistilled Data rather than located within the Prime Data Sieve. This isspecified by Opcode encoding 7 which specifies that the type of Elementis a Prime Data Element that is located Inline in the Distilled Data.The Distilled Data is stored in the data storage system using thisformat. Data in this format is consumed by the Data Retriever 111, sothat the various components of the data can be fetched and subsequentlyreconstituted.

FIGS. 1I through 1P illustrate the conceptual transformation of InputData into the losslessly reduced form for the variations of the methodsand apparatuses for data reduction shown in FIG. 1A through FIG. 1G.FIG. 1I illustrates how a stream of Input Data is factorized intocandidate elements, and subsequently candidate elements are deemed to beeither Prime Data Elements or Derivative Elements. Lastly, the data istransformed into the losslessly reduced form. FIGS. 1I through 1N showvariations of the losslessly reduced form for the various embodiments.

FIG. 1I and FIG. 1J show examples of the losslessly reduced form of thedata produced by the methods and apparatuses illustrated in FIG. 1A. Thelosslessly reduced form in FIG. 1I includes the Content AssociativeMapper and is the form that enables continuous further ingestion of dataand reduction of this data against the existing Prime Data Elements,Meanwhile the losslessly reduced form in FIG. 1J no longer retains theContent Associative Mapper, leading to a smaller footprint of the data.FIG. 1K and FIG. 1L show examples of the losslessly reduced form of thedata produced by the methods and apparatuses illustrated in FIG. 1C. Thelosslessly reduced form in FIG. 1K includes the Content AssociativeMappers and is the form that enables continuous further ingestion ofdata and reduction of this data against the existing Prime Data Elementsand Prime Reconstitution Programs, Meanwhile the losslessly reduced formin FIG. 1L no longer retains the Content Associative Mappers, leading toa smaller footprint of the data.

FIG. 1M and FIG. 1N show examples of the losslessly reduced form of thedata produced by the methods and apparatuses illustrated in FIG. 1F,where Prime Data Elements and Prime Reconstitution Programs are locatedinline in the Distilled Data. The losslessly reduced form in FIG. 1Mincludes the Content Associative Mappers and is the form that enablescontinuous further ingestion of data and reduction of this data againstthe existing Prime Data Elements and Prime Reconstitution Programs,Meanwhile the losslessly reduced form in FIG. 1N no longer retains theContent Associative Mappers, leading to a smaller footprint of the data.FIG. 1O and FIG. 1P show examples of the losslessly reduced form of thedata produced by the methods and apparatuses illustrated in FIG. 1G,where Prime Data Elements and Prime Reconstitution Programs may belocated either inline in the Distilled Data or in the Prime Data Sieve.The losslessly reduced form in FIG. 1O includes the Content AssociativeMappers and is the form that enables continuous further ingestion ofdata and reduction of this data against the existing Prime Data Elementsand Prime Reconstitution Programs, Meanwhile the losslessly reduced formin FIG. 1P no longer retains the Content Associative Mappers, leading toa smaller footprint of the data.

In variations of the embodiments shown in FIGS. 1A through P, thevarious components of the Reduced Data may be further reduced orcompressed using techniques known in the prior art (such as HuffmanCoding, and Lempel Ziv methods) and stored in this compressed form.These components can be subsequently decompressed when they are neededfor use in the Data Distillation Apparatus. This has the benefit offurther reducing the overall footprint of the data.

FIG. 2 illustrates a process for data reduction by factorizing inputdata into elements and deriving these elements from Prime Data Elementsresiding in a Prime Data Sieve in accordance with some embodimentsdescribed herein. As input data arrives, it can be parsed and factorizedor broken up into a series of candidate elements (operation 202). Thenext candidate element is consumed from the input (operation 204), and acontent-associative lookup of the Prime Data Sieve is performed based onthe content of the candidate element to see if there are any suitableelements from which the candidate element can be derived (operation206). If the Prime Data Sieve does not find any such elements (“No”branch of operation 208), the candidate element will be allocated andentered into the Sieve as a new Prime Data Element, and the entry in thedistilled data created for the candidate element will be a reference tothe newly created Prime Data Element (operation 216). If thecontent-associative lookup of the Prime Data Sieve does yield one ormore suitable elements from which the candidate may potentially bederived (“Yes” branch of operation 208), analysis and computation isperformed on the retrieved Prime Data Elements to derive the candidateelement from them. Note that in some embodiments only metadata for thesuitable Prime Data Elements is fetched first and analysis is performedon the metadata, with the suitable Prime Data Elements beingsubsequently fetched only if deemed useful (in these embodiments themetadata for a Prime Data Element provides some information about thecontent of the Prime Data Element, thereby allowing the system toquickly rule out matches or assess derivability based on the metadata).In other embodiments, the Prime Data Sieve retrieves the Prime DataElements directly (i.e., without first retrieving the metadata toanalyze the metadata before retrieving the Prime Data Element) soanalysis and computation is performed on the retrieved Prime DataElements.

A first check is performed to see if the candidate is a duplicate of anyof these elements (operation 210). This check can be sped up using anysuitable hashing technique. If the candidate is identical to a PrimeData Element retrieved from the Prime Data Sieve (“Yes” branch ofoperation 210), the entry in the distilled data created for thecandidate element is replaced by a reference to this Prime Data Elementand an indication that this entry is a Prime Data Element (operation220). If no duplicate is found (“No” branch of operation 210), theentries retrieved from the Prime Data Sieve based on the candidateelement are regarded as entries from which the candidate element ispotentially derivable. The following is an important, novel, andnon-obvious feature of the Prime Data Sieve: when a duplicate is notfound in the Prime Data Sieve, the Prime Data Sieve can return PrimeData Elements that, although not identical to the candidate element, areelements from which the candidate element may potentially be derived byapplying one or more transformations to the Prime Data Element(s). Theprocess can then perform analysis and computation to derive thecandidate element from either the most suitable Prime Data Element or aset of suitable Prime Data Elements (operation 212). In someembodiments, the derivation expresses the candidate element as theresult of transformations performed on the one or more Prime DataElements, such transformations being collectively referred to as theReconstitution Program. Each derivation may require its own uniqueprogram to be constructed. In addition to constructing theReconstitution Program, the process can also compute a distance metricthat generally indicates a level of storage resources and/orcomputational resources that are required to store the reformulation ofthe candidate element and to reconstitute the candidate element from thereformulation. In some embodiments, the footprint of the DerivativeElement is used as a measure of the distance of the candidate from thePrime Data Element(s)—specifically, a Distance metric can be defined asthe sum of the size of the Reconstitution Program plus the size of thereferences to the one or more Prime Data Elements involved in thederivation. The derivation with the shortest Distance can be chosen. TheDistance for this derivation is compared with a Distance Threshold(operation 214), and if the Distance does not exceed the DistanceThreshold, the derivation is accepted (“Yes” branch of operation 214).In order to yield data reduction, the Distance Threshold must always beless than the size of the candidate element. For example, the DistanceThreshold may be set to 50% of the size of the candidate element, sothat a derivative will only be accepted if its footprint is less than orequal to half the footprint of the candidate element, thereby ensuring areduction of 2× or greater for each candidate element for which asuitable derivation exists. The Distance Threshold can be apredetermined percentage or fraction, either based on user-specifiedinput or chosen by the system. The Distance Threshold may be determinedby the system based on static or dynamic parameters of the system. Oncethe derivation is accepted, the candidate element is reformulated andreplaced by the combination of the Reconstitution Program and referencesto the one or more Prime Data Elements. The entry in the distilled datacreated for the candidate element is replaced by the derivation, i.e.,it is replaced by an indication that this is a derivative element, alongwith the Reconstitution Program plus references to the one or more PrimeData Elements involved in the derivation (operation 218). On the otherhand, if the Distance for the best derivation exceeds the DistanceThreshold (“No” branch of operation 214), none of the possiblederivatives will be accepted. In that case, the candidate element may beallocated and entered into the Sieve as a new Prime Data Element, andthe entry in the distilled data created for the candidate element willbe a reference to the newly created Prime Data Element along with anindication that this is a Prime Data Element (operation 216).

Finally, the process can check if there are any additional candidateelements (operation 222), and return to operation 204 if there are morecandidate elements (“Yes” branch of operation 222), or terminate theprocess if there are no more candidate elements (“No” branch ofoperation 222).

A variety of methods can be employed to perform operation 202 in FIG. 2, i.e., to parse the incoming data and break it into candidate elements.The factorization algorithm needs to decide where in the byte stream toinsert breaks to slice up the stream into candidate elements. Possibletechniques include (but are not limited to) breaking up the stream intofixed-sized blocks (such as pages of 4096 bytes), or applying a methodof fingerprinting (such as techniques that apply random primepolynomials to substrings of the input stream) to locate in the datastream suitable fingerprints that become the boundaries of elements(this technique could lead to variable-sized elements), or parsing ofthe input to detect headers or some pre-declared structure anddelineating elements based on this structure. The input could be parsedto detect certain structure that is declared through a schema. The inputcould be parsed to detect the existence of pre-declared patterns,grammars, or regular expressions in the data. Once two consecutivebreaks in the data have been identified, a candidate element is created(the candidate element is the data that is located between the twoconsecutive breaks) and presented to the Prime Data Sieve forcontent-associative lookup. If variable-sized elements are created, thelength of the candidate element needs to be specified and carried asmetadata along with the candidate element.

One important function of the Prime Data Sieve is to providecontent-associative lookup based upon a candidate element presented toit, and to quickly provide one or a small set of Prime Data Elementsfrom which a candidate element can be derived with minimal storageneeded to specify the derivation. This is a difficult problem given alarge dataset. Given terabytes of data, even with kilobyte-sizedelements, there are billions of elements to search and choose from. Theproblem is even more severe on larger datasets. It becomes important toorganize and order the elements using a suitable technique and thendetect similarities and derivability within that organization of theelements, to be able to quickly provide a small set of suitable PrimeData Elements.

The entries in the Sieve could be ordered based upon the value of eachelement (i.e., Prime Data Element), so that all entries could bearranged by value in ascending or descending order. Alternatively, theycould be ordered along a principal axis that is based upon the value ofcertain fields in the element, followed by subordinate axes that use therest of the content of the element. In this context, a field is a set ofcontiguous bytes from the content of the element. Fields could belocated by applying a method of fingerprinting to the contents of theelement so that the location of a fingerprint identifies the location ofa field. Alternatively, certain fixed offsets inside the content of theelement could be chosen to locate a field. Other methods could also beemployed to locate a field, including, but not limited to, parsing theelement to detect certain declared structure, and locating fields withinthat structure.

In yet another form of organization, certain fields or combinations offields within the element could be considered as dimensions, so that aconcatenation of these dimensions followed by the rest of the content ofeach element could be used to order and organize the data elements. Ingeneral, the correspondence or mapping between fields and dimensions canbe arbitrarily complex. For example, in some embodiments exactly onefield may map to exactly one dimension. In other embodiments, acombination of multiple fields, e.g., F1, F2, and F3, may map to adimension. The combining of fields may be achieved either byconcatenating the two fields or by applying any other suitable functionto them. The important requirement is that the arrangement of fields,dimensions, and the rest of the content of an element that is used toorganize elements must enable all Prime Data Elements to be uniquelyidentified by their content and ordered in the Sieve.

In some embodiments, the contents of an element can be represented as anexpression as follows: Element=Head.*sig1.*sig2.*sig1.* . . .sigN.*Tail, where “Head” is a sequence of bytes comprising the leadingbytes of the element, “Tail” is a sequence of bytes comprising theconcluding bytes of the element, and “sig1”, “sig2”, “sig1”, and “sigN”are various signatures or patterns or regular expressions or sequencesof bytes of certain lengths within the body of the content of theelement that characterize the element. The expression “.*” between thevarious signatures is the wildcard expression, i.e., it is the regularexpression notation that allows any number of intervening bytes of anyvalue other than the signature that follows the expression “.*”. In someembodiments, the N-tuple (sig1, sig2, sig1, . . . sigN) is referred toas the Skeletal Data Structure or the Skeleton of the element, and canbe regarded as a reduced and essential subset or essence of the element.In other embodiments, the (N+2)-tuple (Head, sig1, sig2, sig1, . . .sigN, Tail) is referred to as the Skeletal Data Structure or theSkeleton of the element. Alternatively, an N+1 tuple may be employedthat includes either the Head or the Tail along with the rest of thesignatures.

A method of fingerprinting can be applied to the content of the elementto determine the locations of the various components (or signatures) ofthe Skeletal Data Structure within the content of the element.Alternatively, certain fixed offsets inside the content of the elementcould be chosen to locate a component. Other methods could also beemployed to locate a component of the Skeletal Data Structure,including, but not limited to, parsing the element to detect certaindeclared structure, and locating components within that structure. PrimeData Elements can be ordered in the Sieve based on their Skeletal DataStructure. In other words, the various components of the Skeletal DataStructure of the element can be considered as Dimensions, so that aconcatenation of these dimensions followed by the rest of the content ofeach element could be used to order and organize the Prime Data Elementsin the Sieve.

Some embodiments factorize the input data into candidate elements, wherethe size of each candidate element is substantially larger than the sizeof a reference needed to access all such elements in the global dataset.One observation about data that is broken into such data chunks (andthat is being accessed in a content-associative fashion) is that theactual data is very sparse with respect to the total possible valuesthat the data chunk can specify. For example, consider a 1 zettabytedataset. One needs about 70 bits to address every byte in the dataset.At a chunk size of 128 bytes (1024 bits), there are approximately 2⁶³chunks in the 1 zettabyte dataset, so that one needs 63 bits (fewer than8 bytes) to address all of the chunks. Note that an element or chunk of1024 bits could have one of 2¹⁰²⁴ possible values, while the number ofactual values of the given chunks in the dataset is at most 2⁶³ (if allthe chunks are distinct). This indicates that the actual data isextremely sparse with respect to the number of values that can bereached or named by the content of an element. This enables use of atree structure, which is well-suited for organizing very sparse data ina manner that enables efficient content-based lookups, allows newelements to be efficiently added to the tree structure, and iscost-effective in terms of the incremental storage needed for the treestructure itself. Although there are only 2⁶³ distinct chunks in the 1zettabyte dataset, thus requiring only 63 differentiating bits ofinformation to tell them apart, the relevant differentiating bits mightbe spread across the entire 1024 bits of the element and occur atdifferent locations for each element. Therefore, to fully differentiateall the elements, it is insufficient to examine only a fixed 63 bitsfrom the content, but rather the entire content of the element needs toparticipate in the sorting of the elements, especially in a solutionthat provides true content-associative access to any and every elementin the dataset. In the Data Distillation™ framework, it is desirable tobe able to detect derivability within the framework used to order andorganize the data. Keeping all of the above in mind, a tree structurebased upon the content (which progressively differentiates the data asmore of the content is examined) is a suitable organization to order anddifferentiate all the elements in the factorized dataset. Such astructure provides numerous intermediate levels of subtrees which can betreated as groupings of derivable elements or groupings of elements withsimilar properties of derivability. Such a structure can behierarchically augmented with metadata characterizing each subtree orwith metadata characterizing each element of data. Such a structure caneffectively communicate the composition of the entire data it contains,including the density, proximity, and distribution of actual values inthe data.

Some embodiments organize the Prime Data Elements in the Sieve in treeform. Each Prime Data Element has a distinct “Name” which is constructedfrom the entire content of the Prime Data Element. This Name is designedto be sufficient to uniquely identify the Prime Data Element and todifferentiate it with respect to all other elements in the tree. Thereare several ways in which the Name can be constructed from the contentof the Prime Data Element. The Name may be simply comprised of all thebytes of the Prime Data Element, with these bytes appearing in the Namein the same order as they exist in the Prime Data Element. In anotherembodiment, certain fields or combinations of fields referred to asDimensions (where fields and dimensions are as described earlier) areused to form the leading bytes of the Name, with the rest of the contentof the Prime Data Element forming the rest of the Name, so that theentire content of the Prime Data Element is participating to create thecomplete and unique Name of the element. In yet another embodiment, thefields of the Skeletal Data Structure of the element are chosen asDimensions (where fields and dimensions are as described earlier), andare used to form the leading bytes of the Name, with the rest of thecontent of the Prime Data Element forming the rest of the Name, so thatthe entire content of the Prime Data Element is participating to createthe complete and unique Name of the element.

The Name of each Prime Data Element is used to order and organize thePrime Data Elements in the tree. For most practical datasets, even thosethat are very large in size (such as a 1 zettabyte dataset, comprised of2⁵⁸ elements of, say, 4 KB size), it is expected that a small subset ofthe bytes of the Name will often serve to sort and order the majority ofthe Prime Data Elements in the tree.

FIGS. 3A, 3B, 3C, 3D, and 3E illustrate different data organizationsystems that may be used to organize Prime Data Elements based on theirName in accordance with some embodiments described herein.

FIG. 3A shows a trie data structure in which Prime Data Elements areorganized into progressively smaller groups based on the values ofsuccessive bytes from the Name of each Prime Data Element. In theexample shown in FIG. 3A, each Prime Data Element has a distinct Namewhich is constructed from the entire content of the Prime Data Element,and this Name is simply comprised of all the bytes of the Prime DataElement, with these bytes appearing in the Name in the same order asthey exist in the Prime Data Element. The root node of the trierepresents all the Prime Data Elements. Other nodes of the trierepresent subsets or groups of Prime Data Elements. Starting at the rootnode or 1^(st) level of the trie (labelled as Root 302 in FIG. 3A),Prime Data Elements are grouped into subtrees based upon the value ofthe most significant byte of their Name (labelled as N1 in FIG. 3A). AllPrime Data Elements with the same value in the most significant byte oftheir Name will be grouped together into a common subtree, and a linkdenoted by that value will exist from the root node to a noderepresenting that subtree. For example, in FIG. 3A, Node 303 representsa subtree or group of Prime Data Elements that each have the same value2 in their most significant byte N1 of their respective Names. In FIG.3A, this group includes Prime Data Elements 305, 306, and 307.

At the second level of the trie, the second most significant byte of theName of each Prime Data Element is used to further divide each group ofthe Prime Data Elements into smaller subgroups. For example, in FIG. 3A,the group of Prime Data Elements represented by Node 303 is furthersubdivided into subgroups using the second most significant byte N2.Node 304 represents the subgroup of Prime Data Elements which have thevalue 2 in their most significant byte N1, and also the value 1 in theirsecond most significant byte N2 of their respective Names. This subgroupincludes Prime Data Elements 305 and 306.

The process of subdivision continues at each level of the trie creatinglinks from a parent node to each child node, where a child noderepresents a subset of the Prime Data Elements represented by the parentnode. This process continues until there are only individual Prime DataElements at the leaves of the trie. A leaf node represents a group ofleaves. In FIG. 3A, Node 304 is a leaf node. The group of Prime DataElements represented by Node 304 comprises Prime Data Elements 305 and306. In FIG. 3A, this group is further subdivided into individual PrimeData Elements 305 and 306 using the third most significant byte of theirNames. The value of N3=3 leads to Prime Data Elements 305, while thevalue N3=5 leads to Prime Data Element 306. In this example, out oftheir complete Names, only 3 significant bytes are sufficient to fullyidentify Prime Data Elements 305 and 306. Likewise, only two significantbytes from the Name are sufficient to identify Prime Data Element 307.

This example illustrates how, in the given mix of Prime Data Elements,only a subset of the bytes of the Name serves to identify Prime DataElements in the tree, and the entire Name is not needed to arrive at aunique Prime Data Element. Also, Prime Data Elements or groups of PrimeData Elements might each require a different number of significant bytesto be able to uniquely identify them. Thus, the depth of the trie fromthe root node to a Prime Data Element could vary from one Prime DataElement to another. Furthermore, in the trie, each node might have adifferent number of links descending to subtrees below.

In such a trie, each node has a name comprised of the sequence of bytesthat specifies how to reach this node. For example, the name for Node304 is “21”. Also, the subset of bytes from the Name of the element thatuniquely identifies the element in the current distribution of elementsin the tree is the “Path” to this Prime Data Element from the root node.For example, in FIG. 3A, Path 301 with a value of 213 identifies PrimeData Elements 305.

The trie structure described here may create deep trees (i.e., treesthat have many levels) since every differentiating byte of the Name ofan element in the tree adds one level of depth to the trie.

Note that the tree data structures in FIGS. 3A-3E have been drawn fromleft to right. Therefore, as we move from the left side of the figure tothe right side of the figure, we move from higher levels of the tree tolower levels of the tree. Below a given node (i.e., toward the right ofa given node in FIGS. 3A-3E), for any child selected by a certain valueof the differentiating byte from the Name, all elements resident in thesubtrees below that child will have the same value in that correspondingbyte in the Name of the element.

We now describe a method for content-associative lookup of the triestructure, given an input candidate element. This method involvesnavigation of the trie structure using the Name of the candidateelement, followed by subsequent analysis and screening to decide what toreturn as the result of the overall content-associative lookup. In otherwords, the trie navigation process returns a first outcome, and thenanalysis and screening is performed on that outcome to determine theresult of the overall content-associative lookup.

To begin the trie navigation process, the value of the most significantbyte from the Name of the candidate element will be used to select alink (denoted by that value) from the root node to a subsequent noderepresenting a subtree of Prime Data Elements with that same value inthe most significant byte of their Names. Proceeding from this node, thesecond byte from the Name of the candidate element is examined and thelink denoted by that value is selected, thus advancing one level deeper(or lower) into the trie and selecting a smaller subgroup of Prime DataElements that now share with the candidate element at least twosignificant bytes from their Names. This process continues until asingle Prime Data Element is reached or until none of the links matchthe value of the corresponding byte from the Name of the candidateelement. Under either of these conditions, the tree navigation processterminates. If a single Prime Data Element is reached, it may bereturned as the outcome of the trie navigation process. If not, onealternative is to report a “miss”. Another alternative is to returnmultiple Prime Data Elements that are in the subtree that is rooted atthe node where the navigation terminated.

Once the trie navigation process has terminated, other criteria andrequirements may be used to analyze and screen the outcome of the trienavigation process to determine what should be returned as the result ofthe content-associative lookup. For example, when either a single PrimeData Element or multiple Prime Data Elements are returned by the trienavigation process, there could be an additional requirement that theyshare a certain minimum number of bytes with the Name of the candidateelement before qualifying to be returned as the result of thecontent-associative lookup (otherwise the content-associative lookupreturns a miss). Another example of a screening requirement could bethat, if the trie navigation process terminates without reaching asingle Prime Data Element so that multiple Prime Data elements (rootedat the node where the trie navigation terminated) are returned as theoutcome of the trie navigation process, then these multiple Prime DataElements will qualify to be returned as the result of the overallcontent-associative lookup only if the number of these elements is fewerthan a certain specified limit (otherwise the content-associative lookupreturns a miss). Combinations of multiple requirements may be employedto determine the result of the content-associative lookup. In thismanner, the lookup process will either report a “miss” or return asingle Prime Data Element, or if not a single Prime Data Element, then aset of Prime Data Elements that are likely to be good starting pointsfor deriving the candidate element.

FIGS. 3B-3E described below relate to variations and modifications tothe tree data structure illustrated in FIG. 3A. Although thesevariations provide improvements and advantages over the trie datastructure illustrated in FIG. 3A, the process for navigating the datastructure is similar to the process described above in reference to FIG.3A. That is, after the tree navigation for the tree data structuresshown in FIGS. 3B-3E terminates, and subsequent analysis and screeningis performed to determine the result of the overall content-associativelookup, the overall process either returns a miss, a single Prime DataElement, or a set of Prime Data Elements that are likely to be goodstarting points for deriving the candidate element.

FIG. 3B illustrates another data organization system that may be used toorganize Prime Data Elements based on their Name. In the example shownin FIG. 3B, each Prime Data Element has a distinct Name, which isconstructed from the entire content of the Prime Data Element, and thisName is simply comprised of all the bytes of the Prime Data Element,with these bytes appearing in the Name in the same order as they existin the Prime Data Element. FIG. 3B shows a more compact structure wherea single link employs multiple bytes (rather than the single byte usedin the trie in FIG. 3A) from the Name of the Prime Data Elements in thesubtree below to create subdivisions or the next level of groupings. Thelinks from parent nodes to child nodes are now denoted by multiplebytes. Further, from any given parent node, each link might employ adifferent number of bytes to differentiate and identify the subtreeassociated with that link. For example, in FIG. 3B, the link from theroot node to Node 308 is differentiated by using 4 bytes (N₁N₂N₃N₄=9845)from the Name, while the link from the root node to Node 309 isdifferentiated by using 3 bytes (N₁N₂N₃=347) from the Name.

Note that, during tree navigation (using content from a given candidateelement), upon arriving at any parent node in the tree, the treenavigation process needs to ensure that sufficient bytes from the Nameof the candidate element are examined to unambiguously decide which linkto choose. To choose a given link, the bytes from the Name of thecandidate must match all the bytes that denote the transition to thatparticular link. Once again, in such a tree, each node of the tree has aname comprised of the sequence of bytes that specifies how to reach thisnode. For example, the name of node 309 can be “347” because itrepresents a group of Prime Data Elements (e.g., elements 311 and 312)with the 3 leading bytes of their Names being “347”. Upon a lookup ofthe tree using a candidate element with the leading 3 bytes of the Namebeing 347, this data pattern causes the tree navigation process to reachnode 309 as shown in FIG. 3B. Once again, the subset of bytes from theName of the element that uniquely identifies the element in the currentmix of elements in the tree is the “Path” to this Prime Data Elementfrom the root node. For example, in FIG. 3B, the sequence of bytes 3475leads to Prime Data Element 312, and uniquely identifies Prime DataElement 312 in the mix of Prime Data Elements shown in that example.

For diverse and sparse data, the tree structure in FIG. 3B can provemore flexible and compact than the trie structure of FIG. 3A.

FIG. 3C illustrates another data organization system that may be used toorganize Prime Data Elements based on their Name. In the example shownin FIG. 3C, each Prime Data Element has a distinct Name, which isconstructed from the entire content of the Prime Data Element, and thisName is simply comprised of all the bytes of the Prime Data Element,with these bytes appearing in the Name in the same order as they existin the Prime Data Element. FIG. 3C shows another variation (to theorganization described in FIG. 3B) that further compacts the tree andgroups elements in a subtree by using regular expressions (wherenecessary and/or useful) to specify the values from the Name of PrimeData Elements that lead to the various links. The use of regularexpressions allows an efficient grouping of elements that share the sameexpression on corresponding bytes under the same subtree; this can thenbe followed by a more local disambiguation of distinct Prime DataElements within the subtree. Also, the use of the regular expressionsallows a more compact way to describe the values of bytes needed to mapthe element to any subtree below. This further reduces the number ofbytes needed to specify the tree. For example, regular expression 318specifies a pattern of 28 consecutive “F”s; if this link is followedduring tree navigation, we may reach element 314, which includes pattern320 that has 28 consecutive “F”s as per regular expression 318.Likewise, the path that reaches element 316 has a link or branch thatuses a regular expression that specifies a pattern with 16 consecutive“0”s. For such a tree, the tree navigation process needs to detect andexecute such regular expressions in order to determine which link tochoose.

FIG. 3D illustrates another data organization system that may be used toorganize Prime Data Elements based on their Name. In the example shownin FIG. 3D, each Prime Data Element has a distinct Name, which isconstructed from the entire content of the Prime Data Element. A methodof fingerprinting is applied to each element to identify locations offields that contain content that evaluates to a chosen fingerprint. Afield at the location of the first fingerprint found in the element istreated as a Dimension and a certain number of bytes (say, x bytes,where x is significantly smaller than the number of bytes in theelement) from this field are extracted and used as the leading bytes ofthe Name of the Element, with the rest of the bytes of the Name beingcomprised of the rest of the bytes of the Prime Data Element andappearing in the same cyclic order as they exist in the Prime DataElement. This Name is used to organize the Prime Data Elements in thetree. In this example, when no fingerprint is detected in an element,the Name is formulated by simply using all the bytes of the element inthe order in which they exist in the element. A separate subtree(denoted by an indication that no fingerprints were found) holds andorganizes all such elements based upon their Names.

For example, as shown in FIG. 3D, a fingerprinting technique can beapplied to Element 338 (which contains t bytes of data viz. B₁B₂B₃ . . .B_(t)) to obtain fingerprint location “Fingerprint 1” at byte B₁₊₁ whichidentifies the field which will be chosen as “Dimension 1.” Next, xbytes from the location identified by “Fingerprint 1” can be extractedto form “Dimension 1” and these x bytes can be used as the leading bytesN₁N₂ . . . N_(x) of the Name of each element in FIG. 3D. Subsequently,the rest of the t−x bytes from element 338 (starting from B_(i+x+1), andlater wrapping around to B₁B₂B₃ . . . B_(i)) are concatenated and usedas the rest of the bytes N_(x+1) N_(x+2) . . . N_(t) of the Name. Whenno fingerprints are found in the element, the Name N₁ N₂ . . . N_(t) issimply B₁B₂B₃ . . . B_(t) from Element 338. Prime Data Elements aresorted and organized in the tree using their Names. For example, PrimeData Element (PDE) 330 is identified and reached after traversing twolevels of the tree using the Path 13654 . . . 06, where the bytes 13654. . . 0 are N₁ N₂ . . . N_(x) which are the bytes from Dimension 1. Aseparate subtree at Node 335, arrived at from the root along link 334(denoted by an indication that no fingerprints were found) holds andorganizes all Prime Data Elements whose content did not evaluate to thechosen fingerprint. Thus, in this organization, some links, e.g., link336, may organize elements using a Name that is comprised of the bytesof the element appearing in the same order as in the element, whileother links, e.g., link 340, may organize elements using a Name that isformulated using fingerprints.

Upon receiving a candidate element, the process applies the sametechnique described above to determine the Name of the candidateelement, and uses this Name to navigate the tree for acontent-associative lookup. Thus, the same and consistent treatment isapplied to Prime Data Elements (upon their installation into the tree)and candidate elements (upon receiving them from the Parser &Factorizer) in order to create their Names. The tree navigation processuses the Name of the candidate element to navigate the tree. In thisembodiment, if no fingerprint is found in the candidate element, thetree navigation process navigates down the subtree that organizes andcontains Prime Data Elements whose content did not evaluate to thefingerprint.

FIG. 3E illustrates another data organization system that may be used toorganize Prime Data Elements based on their Name. In the example shownin FIG. 3E, each Prime Data Element has a distinct Name, which isconstructed from the entire content of the Prime Data Element. A methodof fingerprinting is applied to each element to identify locations offields that contain content that evaluates to either of twofingerprints. The field at the location of the first occurrence of thefirst fingerprint (Fingerprint1 in FIG. 3E) in the element is treated asa first Dimension (Dimension 1), and the field located at the firstoccurrence of the second fingerprint (Fingerprint2 in FIG. 3E) istreated as a second Dimension (Dimension 2). The use of fingerprintingto look for two distinct fingerprints on an element leads to fourpossible scenarios: (1) both fingerprints are found in the element, (2)fingerprint1 is found but fingerprint 2 is not found, (3) fingerprint 2is found but fingerprint 1 is not found, and (4) no fingerprints arefound. Prime Data Elements can be grouped into 4 subtrees correspondingto each of the scenarios. In FIG. 3E, “FP1” denotes the presence ofFingerprint), “FP2” denotes the presence of Fingerprint2, “˜FP1” denotesthe absence of Fingerprint), and “˜FP2” denotes the absence ofFingerprint2.

For each of the 4 scenarios, the Name of an element is created asfollows: (1) When both fingerprints are found, x bytes from the locationidentified by “Fingerprint 1” can be extracted to form “Dimension 1” andy bytes from the location identified by “Fingerprint 2” can be extractedto form “Dimension 2” and these x+y bytes can be used as the leadingbytes N₁N₂ . . . N_(x+y) of the Name of each such element in FIG. 3E.Subsequently, the rest of the t−(x+y) bytes from element 348 areextracted in cyclic fashion (starting after the bytes from the firstdimension) and concatenated and used as the rest of the bytes N_(x+y+1)N_(x+y+2) . . . N_(t) of the Name. (2) When fingerprint 1 is found butnot fingerprint 2, x bytes from the location identified by “Fingerprint1” can be extracted to form the leading dimension, and these x bytes canbe used as the leading bytes N₁N₂ . . . N_(x) of the Name of each suchelement. Subsequently, the rest of the t−x bytes from element 348(starting from B_(i+x+1), and later wrapping around to B₁B₂B₃ . . .B_(i)) are concatenated and used as the rest of the bytes N_(x+1)N_(x+2) . . . N_(t) of the Name. (3) When fingerprint 2 is found but notfingerprint 1, y bytes from the location identified by “Fingerprint 2”can be extracted to form the leading dimension, and these y bytes can beused as the leading bytes N₁N₂ . . . N_(y) of the Name of each suchelement. Subsequently, the rest of the t−y bytes from element 348(starting from B_(1+y+1), and later wrapping around to B₁B₂B₃ B₁) areconcatenated and used as the rest of the bytes N_(y+1) N_(y+2) N_(t) ofthe Name. (4) When no fingerprints are found in the element, the Name N₁N₂ . . . N_(t) is simply B₁B₂B₃ . . . B_(t) from element 348. Thus, aseparate subtree exists for each of these 4 scenarios. The process toextract Name (N₁N₂N₃ . . . N_(t)) for element 348 can be summarized forthe four scenarios as follows:

(1) both Fingerprint1 and Fingerprint2 found:N ₁ −N _(x) ←B _(i+1) −B _(i+x) =xbytes from Dimension 1N _(x+1) −N _(x+y) ←B _(j+1) −B _(+y) =y bytes from Dimension 2N _(x+y+1) . . . N _(t)=Rest of the bytes (from the Candidate Element ofsize t bytes)=B _(i+x+1) B _(i+x+2) B _(i+x+3) . . . B _(j) B _(j+y+1) B_(j+y+2) B _(j+y+3) . . . B _(t) B ₁ B ₂ B ₃ . . . B _(i)

(2) Fingerprint1 found. Fingerprint2 not found:N ₁ −N _(x) ιB _(i+1) −B _(i+x) =x bytes from Dimension 1N _(x+1) . . . N _(t)=Rest of the bytes (from the Candidate Element ofsize t bytes)=B _(i+x+1) B _(i+x+2) B _(i+x+3) . . . B _(t) B ₁ B ₂ B ₃. . . B _(i)

(3) Fingerprint2 found. Fingerprint1 not found:N ₁ −N _(y) ιB _(j+1) −B _(j+y) =y bytes from Dimension 2N _(y+1) . . . N _(t)=Rest of the bytes (from the Candidate Element ofsize t bytes)=B _(j+y+1) B _(j+y+2) B _(j+y+3) . . . B _(t) B ₁ B ₂ B ₃. . . B _(j)

(4) No fingerprints found:N ₁ −N _(x) ←B ₁ −B _(t)

Upon receiving a candidate element, the process applies the sametechnique described above to determine the Name of the candidateelement. In this embodiment, the 4 methods of Name constructiondescribed above (depending upon whether fingerprint 1 and fingerprint 2are found or not) are applied to the candidate element just as they wereto Prime Data Elements when they were entered into the Sieve. Thus, thesame and consistent treatment is applied to Prime Data Elements (upontheir installation into the tree) and to candidate elements (uponreceiving them from the Parser & Factorizer) in order to create theirNames. The tree navigation process uses the Name of the candidateelement to navigate the tree for a content-associative lookup.

If the content-associative lookup is successful, it will yield PrimeData Elements that have the same patterns at the locations of thespecific dimensions as the candidate element. For example, if bothfingerprints are found in the candidate element, the tree navigationprocess will take it down link 354 of the tree, starting from the rootnode. If the candidate element has the pattern “99 . . . 3” as‘Dimension 1” and the pattern “7 . . . 5” as ‘Dimension 2”, the treenavigation process will arrive at Node 334. This reaches a subtreecontaining two Prime Data Elements (PDE 352 and PDE 353), which arelikely targets for the derivation. Additional analysis and screening isperformed (by first examining the metadata, and if needed, bysubsequently fetching and examining the actual Prime Data Elements) todetermine which Prime Data Element is best suited for the derivation.Thus, embodiments described herein identify a variety of tree structuresthat can be used in the Sieve. Combinations of such structures orvariations thereof could be employed to organize the Prime DataElements. Some embodiments organize the Prime Data Elements in treeform, wherein the entire content of the element is used as the Name ofthe element. However, the sequence in which bytes appear in the Name ofthe element is not necessarily the sequence in which the bytes appear inthe element. Certain fields of the element are extracted as dimensionsand used to form the leading bytes of the Name, and the rest of thebytes of the element make up the rest of the Name. Using these Names,the elements are ordered in the Sieve in tree form. The leading digitsof the Name are used to differentiate the higher branches (or links) ofthe tree, and the rest of the digits are used to progressivelydifferentiate all branches (or links) of the tree. Each node of the treecould have a different number of links emanating from that node. Also,each link from a node could be differentiated and denoted by a differentnumber of bytes, and the description of these bytes could beaccomplished through use of regular expressions and other powerful waysto express their specification. All these features lead to a compacttree structure. At the leaf nodes of the tree reside references toindividual Prime Data Elements.

In one embodiment, a method of fingerprinting can be applied to thebytes comprising the Prime Data Element. A number of bytes residing atthe location identified by the fingerprint can be used to make up acomponent of the element Name. One or more components could be combinedto provide a dimension. Multiple fingerprints could be used to identifymultiple dimensions. These dimensions are concatenated and used as theleading bytes of the Name of the element, with the rest of the bytes ofthe element comprising the rest of the Name of the element. Since thedimensions are located at positions identified by fingerprints, itincreases the likelihood that the Name is being formed from consistentcontent from each element. Elements that have the same value of contentat the fields located by the fingerprint will be grouped together alongthe same leg of the tree. In this fashion, similar elements will begrouped together in the tree data structure. Elements with nofingerprints found in them can be grouped together in a separatesubtree, using an alternative formulation of their Names.

In one embodiment, a method of fingerprinting can be applied to thecontent of the element to determine the locations of the variouscomponents (or signatures) of the Skeletal Data Structure (describedearlier) within the content of the element. Alternatively, certain fixedoffsets inside the content of the element could be chosen to locate acomponent. Other methods could also be employed to locate a component ofthe Skeletal Data Structure of the element, including, but not limitedto, parsing the element to detect certain declared structure, andlocating components within that structure. The various components of theSkeletal Data Structure of the element can be considered as Dimensions,so that a concatenation of these dimensions followed by the rest of thecontent of each element is used to create the Name of each element. TheName is used to order and organize the Prime Data Elements in the tree.

In another embodiment, the element is parsed in order to detect certainstructure in the element. Certain fields in this structure areidentified as dimensions. Multiple such dimensions are concatenated andused as the leading bytes of the Name, with the rest of the bytes of theelement comprising the rest of the Name of the element. Since thedimensions are located at positions identified by parsing the elementand detecting its structure, it increases the likelihood that the Nameis being formed from consistent content from each element. Elements thathave the same value of content at the fields located by the parsing willbe grouped together along the same leg of the tree. In this fashion,once again, similar elements will be grouped together in the tree datastructure.

In some embodiments, each node in the tree data structure contains aself-describing specification. Tree nodes have one or more children.Each child entry contains information on the differentiating bytes onthe link to the child, and a reference to the child node. A child nodemay be a tree node or leaf node. FIG. 3F presents a self-describing treenode data structure in accordance with some embodiments describedherein. The tree node data structure shown in FIG. 3F specifies (A)information pertaining to the Path from the root node to this tree node,including all or a subset of the following components: the actualsequence of bytes from the Name to reach this tree node, the number ofbytes of the Name consumed to reach this node from the root node, anindication whether this number of bytes consumed is greater than somepre-specified threshold, and other metadata that describes the Path tothis node and is useful for the content-associative search of the treeas well as for decisions relating to the construction of the tree, (B)the number of children the node has, and (C) for each child (whereineach child corresponds to a branch of the tree) it specifies (1) ChildID, (2) number of differentiating bytes needed from the succeeding bytesof the Name in order to transition down this link of the tree, (3) thespecification for the actual value of the bytes from the Name that takeit down this link, and (4) a reference to the child node.

FIG. 3G presents a self-describing leaf node data structure inaccordance with some embodiments described herein. Leaf nodes have oneor more children. Each child is the link to a Prime Data Element. Eachchild entry contains information on the differentiating bytes on thelink to the Prime Data Element, a reference to the Prime Data Element,count of Duplicates & Derivatives and other metadata about the PrimeData Element. The leaf node data structure shown in FIG. 3G specifies(A) information pertaining to the Path from the root node to this leafnode, including all or a subset of the following components: the actualsequence of bytes from the Name to reach this leaf node, the number ofbytes of the Name consumed to reach this node from the root node, anindication whether this number of bytes consumed is greater than somepre-specified threshold, and other metadata that describes the Path tothis node and is useful for the content-associative search of the treeas well as for decisions relating to the construction of the tree, (B)the number of children the node has, and (C) for each child (whereineach child corresponds to a Prime Data Element under the leaf node) itspecifies (1) Child ID, (2) number of differentiating bytes needed fromthe succeeding bytes of the Name in order to transition down this linkof the tree to a Prime Data Element, (3) the specification for theactual value of the bytes from the Name that take it down this leg, (4)a reference to the Prime Data Element that terminates the tree on thispath of the tree, (5) a count of how many duplicates and derivatives arepointing to this Prime Data Element (this is used to ascertain whetheran entry can be deleted from the Sieve upon a deletion of data in thestorage system), and (6) other metadata for the Prime Data Elementincluding Size of Prime Data Element, etc.

In order to increase the efficiency with which fresh Prime Data Elementsget installed into the tree, some embodiments incorporate an additionalfield into the leaf node data structure for each Prime Data Element thatis kept at the leaf node of the tree. Note that when a fresh element hasto be inserted into the tree, additional bytes of the Name or content ofeach of the Prime Data Elements in the subtree in question might beneeded in order to decide where in the subtree to insert the freshelement, or whether to trigger a further partitioning of the subtree.The need for these additional bytes could require fetching several ofthe Prime Data Elements in question in order to extract the relevantdifferentiating bytes for each of these elements with respect to thefresh element. In order to reduce and optimize (and, in most cases,fully eliminate) the number of IOs needed for this task, the datastructure in the leaf node includes a certain number of additional bytesfrom the Name of each Prime Data Element under that leaf node. Theseadditional bytes are referred to as Navigation Lookahead bytes, andassist in sorting the Prime Data Elements with respect to a freshincoming element. The Navigation Lookahead bytes for a given Prime DataElement are installed into the leaf node structure upon installation ofthe Prime Data Element into the Sieve. The number of bytes to beretained for this purpose could be chosen statically or dynamicallyusing a variety of criteria, including the depth of the subtree involvedand the density of Prime Data Elements in that subtree. For example, forPrime Data Elements being installed at shallow levels of the tree, thesolution may add a longer Navigation Lookahead Field than for Prime DataElements residing in a very deep tree. Also, when a fresh element isbeing installed into the Sieve, and if there are already many Prime DataElements in the existing target subtree (with increased likelihood of animminent repartitioning), then additional Navigation Lookahead bytescould be retained for the fresh Prime Data Element when it is beinginstalled into the subtree.

FIG. 3H presents the leaf node data structure for a leaf node thatincludes the Navigation Lookahead field. This data structure specifies(A) information pertaining to the Path from the root node to this leafnode, including all or a subset of the following components: the actualsequence of bytes from the Name to reach this leaf node, the number ofbytes of the Name consumed to reach this node from the root node, anindication whether this number of bytes consumed is greater than somepre-specified threshold, and other metadata that describes the Path tothis node and is useful for the content-associative search of the treeas well as for decisions relating to the construction of the tree, (B)the number of children the node has, and (C) for each child (whereineach child corresponds to a Prime Data Element under the leaf node) itspecifies (1) Child ID, (2) number of differentiating bytes needed fromthe succeeding bytes of the Name in order to transition down this linkof the tree to a Prime Data Element, (3) the specification for theactual value of the bytes that take it down this leg, (4) a reference tothe Prime Data Element that terminates the tree on this path of thetree, (5) the Navigation Lookahead fields that specify how many bytes ofNavigation Lookahead are retained for the Prime Data Element, as well asthe actual values of those bytes, (6) a count of how many duplicates andderivatives are pointing to this Prime Data Element (this is used toascertain whether an entry can be deleted from the Sieve upon a deletionof data in the storage system), and (7) other metadata for the PrimeData Element including size of Prime Data Element, etc.

In some embodiments, the various branches of the tree are used to mapthe various data elements into groups or ranges formed by interpretingthe differentiating bytes along a link leading to a child subtree as arange delimiter. All elements in that child subtree will be such thatthe values of the corresponding bytes in the element will be less thanor equal to the values for the differentiating bytes specified for thelink to the particular child subtree. Thus each subtree will nowrepresent a group of elements whose values fall within a specific range.Within a given subtree, each subsequent level of the tree willprogressively divide the set of elements into smaller ranges. Thisembodiment provides a different interpretation to the components of theself-describing tree node structure shown in FIG. 3F. The N children inFIG. 3F are ordered by value of their differentiating bytes in the treenode data structure and represent an ordered sequence of non-overlappingranges. For N nodes, there are N+1 ranges—the lowest or 1^(st) rangecomprises of values less than or equal to the smallest entry and theN+1th range comprises of values greater than the Nth entry. The N+1thrange will be treated as out of range, so that the N links lead to Nsubtrees or ranges below.

For example, in FIG. 3F, Child 1 defines the lowest range and uses 6bytes (of value abef12d6743a) to differentiate its range—the range forChild 1 is from 00000000 to abef12d6743a. If the corresponding 6 bytesof the candidate element fall within this range, inclusive of the endvalues, the link for this child will be chosen. If the corresponding 6leading bytes of the candidate element are larger than the rangedelimiter abef12d6743a, Child 1 will not be selected. To examine whetherthe candidate falls within the range for Child 2, two conditions must besatisfied—firstly the candidate must be outside the range for theimmediately preceding child (Child 1 in this example), and secondly thecorresponding bytes in its Name must be less than or equal to the rangedelimiter for Child 2. In this example, the range delimiter for Child 2is described by 2 bytes of value dcfa. Hence the 2 corresponding bytesfor the candidate element must be less than or equal to dcfa. Using thismethod, the candidate element and all the children in the tree node canbe examined to check which of the N+1 ranges the candidate element fallsin. For the example shown in FIG. 3F, a miss condition will be detectedif the 4 corresponding bytes of the Name of the candidate element aregreater than the value of the differentiating bytes for the link forChild N, which is f3231929.

The tree navigation process can be modified to accommodate this newrange node. Upon arriving at a range node, to choose a given linkemanating from that node, the bytes from the Name of the candidate mustfall within the range defined for that particular link. If the value ofthe bytes from the Name of the candidate is larger than the value of thecorresponding bytes in all the links, the candidate element fallsoutside of all ranges spanned by the subtree below—in this case(referred to as an “out of range condition”) a miss condition isdetected and the tree navigation process terminates. If the leadingbytes of the Name of the candidate element fall within the rangedetermined by the corresponding differentiating bytes along a linkleading to the child subtree, tree navigation continues to that subtreebelow. Unless it terminates due to an “out of range condition”, treenavigation can progressively continue deeper down the tree until itreaches a leaf node data structure.

This kind of range node can be employed in the tree structure inconjunction with the trie nodes described in FIGS. 3A-3E. In someembodiments, a certain number of levels of upper nodes of the treestructure can be trie nodes with tree traversal being based on exactmatches between the leading bytes of the Name of the candidate elementand the corresponding bytes along a link of the tree. Subsequent nodescan be range nodes with tree traversal dictated by the range in whichthe corresponding bytes of the Name of the candidate element falls. Upontermination of the tree navigation process, as described earlier in thisdocument, a variety of criteria can be used to decide what to return asthe result of the overall content associative lookup.

The foregoing descriptions of methods and apparatuses for representingand using tree nodes and leaf nodes have been presented only forpurposes of illustration and description. They are not intended to beexhaustive or to limit the present invention to the forms disclosed.Accordingly, many modifications and variations will be apparent topractitioners skilled in the art.

Upon being presented a candidate element as input, the tree node andleaf node structures described above can be traversed and acontent-associative lookup of the tree can be performed based upon thecontent of the candidate element. The Name of the candidate element willbe constructed from the bytes of the candidate element just as the Nameof a Prime Data Element was constructed from the content of the PrimeData Element when it was installed in the Sieve. Given an inputcandidate element, the method for content-associative lookup of the treeinvolves navigation of the tree structure using the Name of thecandidate element, followed by subsequent analysis and screening todecide what to return as the result of the overall content-associativelookup. In other words, the tree navigation process returns a firstoutcome, and then analysis and screening is performed on that outcome todetermine the result of the overall content-associative lookup.

If there are any Prime Data Elements with the same leading bytes of Nameas the candidate (or bytes such that they fall within the same range),the tree will identify that subset of Prime Data Elements in the form ofa subtree of elements denoted by a link. In general, each tree node orleaf node can store information that enables the tree navigation processto determine which outgoing link, if any, is to be selected to navigateto the next lower level in the tree based upon the corresponding bytesof the Name of the input element, and the identity of the node that isreached when the tree is navigated along the selected link. If each nodecontains this information, then the tree navigation process canrecursively navigate down each level in the tree until no matches arefound (at which point the tree navigation process can return a set ofPrime Data Elements that exists in the subtree rooted at the currentnode) or a Prime Data Element is reached (at which point the treenavigation process can return the Prime Data Element and any associatedmetadata).

Once the tree navigation process has terminated, other criteria andrequirements may be used to analyze and screen the outcome of the treenavigation process to determine what should be returned as the result ofthe overall content-associative lookup. First, one could pick the PrimeData Element with the most number of leading bytes from the Name incommon with the candidate. Second, when either a single Prime DataElement or multiple Prime Data Elements are returned by the treenavigation process, there could be an additional requirement that theyshare a certain minimum number of bytes with the Name of the candidateelement before qualifying to be returned as the result of thecontent-associative lookup (otherwise, the content-associative lookupreturns a miss). Another example of a screening requirement could bethat, if the tree navigation process terminates without reaching asingle Prime Data Element so that multiple Prime Data elements (rootedat the node where the tree navigation terminated) are returned as theoutcome of the tree navigation process, then these multiple Prime DataElements will qualify to be returned as the result of the overallcontent-associative lookup only if the number of these elements is fewerthan a certain specified limit such as 4-16 elements (otherwise, thecontent-associative lookup returns a miss). Combinations of multiplerequirements may be employed to determine the result of thecontent-associative lookup. If multiple candidates still remain, onecould examine Navigation Lookahead bytes and also associated metadata todecide which Prime Data Elements are the most suitable. If still unableto narrow the choice down to a single Prime Data Element, one couldfurnish multiple Prime Data Elements to the Derive function. In thismanner, the lookup process will either report a “miss,” or return asingle Prime Data Element, or if not a single Prime Data Element, then aset of Prime Data Elements that are likely to be good starting pointsfor deriving the candidate element.

The tree needs to be designed for efficient content-associative access.A well-balanced tree will provide a comparable depth of access for muchof the data. It is expected that the upper few levels of the tree willoften be resident in the processor cache, the next few levels in fastmemory, and the subsequent levels in flash storage. For very largedatasets, it is possible that one or more levels need to reside in flashstorage and even disk.

FIG. 4 shows an example of how 256 TB of prime data may be organized intree form, and presents how the tree may be laid out in memory andstorage in accordance with some embodiments described herein. Assumingan average fanout of 64 (which is 2⁶) children per node, the referencefor a Prime Data Element can be accessed by reaching a leaf node datastructure (e.g., as described in FIG. 3H) which is resident at (onaverage) the 6th level of the tree (i.e., after 5 link traversals orhops). So, such a structure at the 6th level of the tree, after 5 hops,will reside alongside another 2³⁰ such nodes, each with an average of 64children (these children are the references to the Prime Data Elements),thus accommodating approximately 64 billion Prime Data Elements. At anelement size of 4 KB, this accommodates 256 TB of Prime Data Elements.

The tree can be laid out so that the 6 levels of the tree can betraversed as follows: 3 levels residing in on-chip cache (containingapproximately four thousand “upper level” tree node data structuresspecifying transitions for links to approximately 256 K nodes), 2 levelsin memory (containing 16 million “middle level” tree node datastructures specifying transitions for links to 1 billion leaf nodesapproximately), and the 6th level in flash storage (accommodating abillion leaf node data structures). The 1 billion leaf node datastructures resident at this 6th level of the tree in flash storagefurnish the references for the 64 billion Prime Data Elements (onaverage 64 elements per leaf node).

In the example shown in FIG. 4 , at the 4th and 5th levels, each nodedevotes on average 16 bytes per element (1 byte for child ID, e.g., a6-byte reference to the PDE, plus a byte for byte count, plus 8 bytes onaverage to specify actual transition bytes as well as some metadata). Atthe 6th level, each leaf node devotes on average 48 bytes per element (1byte for child ID, 1 byte for byte count, 8 bytes to specify actualtransition bytes, 6-byte reference to the Prime Data Element, 1 byte forcount of derivatives off this Prime Data Element, 16 bytes of NavigationLookahead, 2 bytes for size of Prime Data Element, as well as 13 bytesof other metadata), thus the total capacity in flash storage requiredfor the tree (including the references to the Prime Data Elements, andincluding any metadata) is about 3 Terabytes. The total capacityrequired for the upper nodes of the tree is a smaller fraction of thissize (since there are fewer nodes, and fewer bytes are needed to specifythe tighter reference to the children nodes, and less metadata isrequired per node). In the example, the upper tree nodes devote onaverage 8 bytes per element (1 byte for child ID, 1 byte for byte count,plus 3-4 bytes on average to specify actual transition bytes, and 2-3byte reference to the child node). Overall, in this example, a syntheticdataset with 256 TB of prime data is sorted into one billion groupsusing 3 TB (or 1.17% of 256 TB) of additional apparatus.

In the example shown in FIG. 4 , where 256 TB of prime data contains 64billion Prime Data Elements of 4 KB each, one needs fewer than 5 bytes(or 36 bits) of address to fully differentiate the 64 billion Prime DataElements. From a content-associative standpoint, if the mix of data issuch that an average of 4 bytes of progressive Name are consumed at eachof the first 3 levels, and 8 bytes at each of the next 3 levels, a totalof 36 bytes (288 bits) of Name (on average) would differentiate all the64 billion Prime Data Elements. These 36 bytes would be less than 1% ofthe 4 KB that make up each element. If a Prime Data Element of 4 KB canbe identified by 1% (or even 5-10%) of its bytes, then the rest of thebytes (which make up the majority of the bytes) could tolerateperturbations, and a candidate with such perturbations could still reachthis Prime Data Element and be considered for derivation from it.

Note that the number of bytes needed on any given link (to differentiatethe various subtrees below) will be governed by the actual data in themix of elements that comprise the dataset. Likewise, the number of linksout of a given node will also vary with the data. The self-describingtree node and leaf node data structures will declare the actual numberand the values of the bytes needed for each link, as well as the numberof links emanating from any node.

Further controls can be placed to limit the amount of cache, memory, andstorage devoted at the various levels of the tree, to sort the inputinto as many differentiated groups as possible, within the allocatedbudget of incremental storage. To handle situations where there aredensities and pockets of data that require very deep subtrees to fullydifferentiate the elements, such densities could be handled efficientlyby grouping a larger set of related elements into a flat group at acertain depth (e.g. the 6^(th) level) of the tree and performing astreamlined search and derivation upon these (by first examining theNavigation Lookahead and metadata to determine the best Prime DataElement, or else (as a fallback) looking only for duplicates rather thanthe full derivation that is afforded by the method for the rest of thedata). This would circumvent the creation of very deep trees. Anotheralternative is to allow deep trees (with many levels) as long as theselevels fit in available memory. The moment the deeper levels spill outto flash or disk, steps can be taken to flatten the tree from that levelonwards, to minimize the latency that would otherwise be incurred bymultiple successive accesses to deeper levels of tree nodes stored inflash or disk,

It is expected that a relatively small fraction of the total bytes fromthe Name of the element will often be sufficient to identify each PrimeData Element. Studies performed on a variety of real world datasetsusing the embodiments described herein confirm that a small subset ofthe bytes of a Prime Data Element serves to order the majority of theelements to enable the solution. Thus, such a solution is efficient interms of the amount of storage that it requires for its operation.

In terms of accesses needed for the example from FIG. 4 , once for everyincoming 4 KB chunk of input (or candidate element), the scheme willneed the following accesses to query the tree structure and reach a leafnode: three cache references, two memory references (or perhaps multiplememory references), plus a single IO from flash storage to access theleaf node data structure. This single IO from storage would fetch a 4 KBpage which would hold information for the leaf node data structure for agroup of approximately 64 elements which would include the 48 bytesdevoted to the Prime Data Element in question. These 48 bytes wouldinclude metadata on the Prime Data Element in question. This wouldconclude the tree lookup process. Subsequently, the number of IOs neededwould depend upon whether the candidate element turns out to be aduplicate, a derivative, or a fresh Prime Data Element to be installedin the Sieve.

A candidate element that is a duplicate of a Prime Data Element willneed 1 IO to fetch the Prime Data Element in order to verify theduplicate. Once the duplicate is verified, there will be one more IO toupdate the metadata in the tree. Hence, ingestion of duplicate elementswill need two IOs after the tree lookup, for a total of 3 IOs.

A candidate element that fails the tree lookup and is neither aduplicate nor a derivative requires 1 more IO to store the element as anew Prime Data Element in the Sieve, and another IO to update themetadata in the tree. Thus, ingestion of a candidate element that failsthe tree lookup will require 2 IOs after the tree lookup, leading to atotal of 3 IOs. However, for candidate elements where the tree lookupprocess terminates without needing a storage JO, a total of only 2 IOsis needed for ingesting such candidate elements.

A candidate element that is a derivative (but not a duplicate) willfirst need 1 IO to fetch the Prime Data Element needed to compute thederivation. Since it is expected that most often derivations will be offa single Prime Data Element (rather than multiple), only a single IOwill be needed to fetch the Prime Data Element. Subsequent to successfulcompletion of the derivation, 1 more IO will be needed to store theReconstitution Program and the derivation details in the entry createdfor the element in storage, and another IO to update the metadata in thetree (such as counts, etc.) to reflect the new derivative. Hence,ingestion of a candidate element that becomes a derivative requires 3additional IOs after the first tree lookup for a total of 4 IOs.

In summary, to ingest a candidate element and apply the DataDistillation™ method to it (while exploiting redundancy globally acrossa very large dataset) requires approximately 3 to 4 IOs. Compared towhat is needed by traditional data deduplication techniques, this istypically just one more IO per candidate element, in return for whichredundancy can be exploited globally across the dataset at a grain thatis finer than the element itself.

A storage system that offers 250,000 random IO accesses/sec (which meansbandwidth of 1 GB/sec of random accesses to pages of 4 KB) could ingestand perform the Data Distillation™ method on about 62,500 input chunksper second (250,000 divided by 4 IOs per input chunk of average size 4KB each). This enables an ingest rate of 250 MB/sec while using up allthe bandwidth of the storage system. If only half of the bandwidth ofthe storage system is used (so that the other half is available foraccesses to the stored data), such a Data Distillation™ system couldstill deliver ingest rates of 125 MB/sec. Thus, given sufficientprocessing power, Data Distillation™ systems are able to exploitredundancy globally across the dataset (at a grain that is finer thanthe element itself) with an economy of IOs and deliver data reduction atingest rates in the hundreds of megabytes per second on contemporarystorage systems.

Thus, as confirmed by the test results, embodiments described hereinachieve the complex task of searching for elements (from which an inputelement can be derived with minimal storage needed to specify thederivation) from a massive store of data with an economy of IO accessesand with minimal incremental storage needed for the apparatus. Thisframework thus constructed makes it feasible to find elements suitablefor derivation using a smaller percentage of the total bytes of theelement, leaving the bulk of the bytes available for perturbation andderivation. An important insight that explains why this scheme workseffectively for much data is that the tree provides a wieldy,fine-grained structure that allows one to locate the differentiating anddistinguishing bytes that identify elements in the Sieve, and althoughthese bytes are each at different depths and positions in the data, theycan be isolated and stored efficiently in the tree structure.

FIGS. 5A-5C illustrate an actual example of how data can be organizedusing embodiments described herein. FIG. 5A illustrates 512 bytes ofinput data, and the result of factorization (e.g., the result ofperforming operation 202 in FIG. 2 ). In this example fingerprinting isapplied to determine breaks in the data, so that consecutive breaksidentify candidate elements. Alternating candidate elements have beenshown using bold and regular font. For example, the first candidateelement is“b8ac83d9dc7caf18f2f2e3f783a0ec69774bb50bbe1d3ef1ef8a82436ec43283bc1c0f6a82e19c224b22f9b2,” and the next candidate element is“ac83d9619ae5571ad2bbcc15d3e493eef62054b05b2dbccce933483a6d3daab3cb19567dedbe33e952a966c49f3297191cf22aa31b98b9dcd0fb54a7f761415e,” and so forth. The input in FIG. 5A isfactorized into 12 variable-sized candidate elements as shown. Theleading bytes of each chunk are used to order and organize elements inthe Sieve. FIG. 5B illustrates how the 12 candidate elements shown inFIG. 5A can be organized as Prime Data Elements in the Sieve in treeform using their Names, and using a tree structure described in FIG. 3B.Each element has a distinct Name, constructed from the entire content ofthe element. In this example, since fingerprinting is applied todetermine the breaks between the 12 candidate elements, the leadingbytes of each candidate element will already be aligned to an anchorfingerprint; hence, the leading bytes of each Name will already havebeen constructed from a first dimension of content anchored at thisfingerprint. The leading bytes of the Name organize the variouselements. For example, if the first byte in the Name of the element isequal to “0×22” then the top link is taken to select Prime Data Element#1. Note that various links in FIG. 5B are differentiated using avarying number of bytes as explained in reference to the tree datastructure illustrated in FIG. 3B.

FIG. 5C illustrates how the 12 candidate elements shown in FIG. 5A canbe organized using a tree data structure described in reference to FIG.3D. Fingerprinting is further applied to the content of each element toidentify a secondary fingerprint within the content of the element.Bytes of content extracted from the location of the first fingerprint(already existing at the boundary of each element) and secondfingerprint are concatenated to form the leading bytes of the Name,which are used to organize the elements. In other words, the elementName is constructed as follows: bytes of data from two dimensions orfields (located by an anchor fingerprint and a secondary fingerprintrespectively) are concatenated to form the leading bytes of the Name,followed by the rest of the bytes. As a consequence of this choice ofconstruction of the Name, a different sequence of bytes leads to thevarious Prime Data Elements in FIG. 5C (vs. FIG. 5B). For example, toreach Prime Data Element #4, the tree navigation process first takes thelink corresponding to “46093f9d” which are the leading bytes of thefield at the first dimension (i.e., the first fingerprint), and thentakes the link corresponding to “c4” which is the leading byte of thefield located at the second dimension (i.e., the second fingerprint).

FIGS. 6A-6C show how tree data structures can be used forcontent-associative mappers 121 and 122 described in reference to FIGS.1A-1C, respectively, in accordance with some embodiments describedherein.

Once the difficult problem of finding suitable Prime Data Elements (fromwhich to attempt to derive the candidate element) has been solved, theproblem is narrowed down to examining one or a small subset of PrimeData Elements and optimally deriving the candidate element from themwith minimum storage needed to specify the derivation. Other objectivesinclude keeping the number of accesses to the storage system to aminimum, and keeping the derivation time and the reconstitution timeacceptable.

The Deriver must express the candidate element as the result oftransformations performed on the one or more Prime Data Elements, andmust specify these transformations as a Reconstitution Program whichwill be used to regenerate the derivative upon data retrieval. Eachderivation may require its own unique program to be constructed. Thefunction of the Deriver is to identify these transformations and createthe Reconstitution Program with the smallest footprint. A variety oftransformations could be employed, including arithmetic, algebraic, orlogical operations performed upon the one or more Prime Data Elements orupon specific fields of each Element. Additionally, one could use bytemanipulation transformations, such as the concatenation, insertion,replacement, and deletion of bytes in the one or more Prime DataElements.

FIG. 7A provides an example of the transformations that could bespecified in the Reconstitution Program in accordance with someembodiments described herein. The vocabulary of transformationsspecified in this example includes arithmetic operations on fields ofspecified length in the element, as well as insertions, deletions,appends, and replacements of a declared length of bytes at specifiedoffsets in the Prime Data Element. A variety of techniques andoperations could be employed by the Deriver to detect the similaritiesand the differences between the candidate element and the one or morePrime Data Elements, and to construct the Reconstitution Program. TheDeriver could exploit the vocabulary available in the underlyinghardware to perform its function. The end result of the work is tospecify the transformations in the vocabulary specified for theReconstitution Program, and to do so using a minimal amount ofincremental storage and in a manner that also enables fast dataretrieval.

The Deriver could avail of the processing power of the underlyingmachine and work within the processing budget allocated to it to providethe best analysis possible within the cost-performance constraints ofthe system. Given that microprocessor cores are more readily available,and given that IO accesses to storage are expensive, the DataDistillation™ solution has been designed to take advantage of theprocessing power of contemporary microprocessors to efficiently performlocal analysis and derivation of the content of the candidate elementoff a few Prime Data Elements. It is expected that the performance ofthe Data Distillation™ solution (on very large data) will berate-limited not by the computational processing but by the TO bandwidthof a typical storage system. For example, it is expected that a coupleof microprocessor cores will suffice to perform the required computationand analysis to support ingest rates of several hundred megabytes persecond on a typical flash-based storage system supporting 250,000IOs/sec. Note that two such microprocessor cores from a contemporarymicroprocessor such as the Intel Xeon Processor E5-2687 W (10 cores, 3.1GHz, 25 MB cache) is a fraction (two of ten) of the total computationalpower available from the processor.

FIG. 7B shows examples of the results of candidate elements beingderived from Prime Data Elements in accordance with some embodimentsdescribed herein. Specifically, the data pattern “Elem” is the PrimeData Element that is stored in the Prime Data Sieve, and the datapattern “Cand” is the candidate element that is to be derived from thePrime Data Element. The 18 common bytes between “Cand” and “Elem” havebeen highlighted. Reconstitution program 702 specifies how data pattern“Cand” can be derived from data pattern “Elem.” As shown in FIG. 7B,Reconstitution program 702 illustrates how to derive “Cand” from “Elem”by using 1 byte Replace, 6 bytes Insert, 3 bytes Delete, 7 bytes bulkReplace. Cost to specify the derivative is 20 bytes+3 byte reference=23bytes, which is 65.71% of the original size. Note that theReconstitution Program 702 shown is a human-readable representation ofthe program and may not be how the program is actually stored byembodiments described herein. Likewise other Reconstitution Programsbased on arithmetic operations such as multiplication and addition havealso been shown in FIG. 7B. For example, if “Elem” isbc1c0f6a790c82e19c224b22f900ac83d9619ae5571ad2bbec152054ffffff83 and“Cand” isbc1c0f6a790c82e19c224b22f91c4da1aa0369a0461ad2bbec152054ffffff83, thenthe 8-byte difference can be derived as shown using multiply(00ac83d9619ae557)*2a=[00]1c4da1aa0369a046. The cost to specify thederivative: 4 bytes+3 byte reference=7 bytes, which is 20.00% of theoriginal size. Alternatively, if “Elem” isbc1c0f6a790c82e19c224b22f9b2ac83ffffffffffffffffffffffffffffb283, and“Cand” isbc1c0f6a790c82e19c224b22f9b2ac8300000000000000000000000000002426, thenthe 16-byte difference can be derived as shown using addition, e.g., byadding 0x71a3 to the 16-byte region starting at offset 16, anddiscarding the carry. The cost to specify the derivative is 5 bytes+3byte reference=8 bytes, which is 22.85% of the original size. Note thatthe sample encodings in FIG. 7A have been chosen for illustrationpurposes only. The examples in FIG. 7B have data sizes of 32 bytes, andso 5 bits suffice for the length and offset fields within the element.For large elements (e.g., a 4 KB element), the sizes of these fieldswould need be increased to 12 bits. Likewise, the sample encodingaccommodates a reference size of 3 bytes or 24 bits. This should allow16 million Prime Data Elements to be referenced. If the reference needsto be able to address any location in, say, 256 TB of data, thereference would need to be 6 bytes in size. When such a dataset isfactorized into 4 KB elements, the 6 bytes needed to specify thereference will be a small fraction of the size of the 4 KB element.

The size of the information needed to specify the Derivative element(that is derived from the one or more Prime Data Elements) is the sum ofthe size of the Reconstitution Program and the size of the referencesneeded to specify the required (one or more) Prime Data Elements. Thesize of the information needed to specify a candidate element as aDerivative element is referred to as the Distance of the candidate fromthe Prime Data Element. When the candidate can be feasibly derived fromany one set of multiple sets of Prime Data Elements, the set of PrimeData Elements with the shortest Distance is chosen as the target.

When the candidate element needs to be derived from more than one PrimeData Element (by assembling extracts derived from each of these), theDeriver needs to factor in the cost of the additional accesses to thestorage system and weigh that against the benefit of a smallerReconstitution Program and a smaller Distance. Once an optimalReconstitution Program has been created for a candidate, its Distance iscompared with the Distance Threshold; if it does not exceed thethreshold, the derivation is accepted. Once a derivation is accepted,the candidate element is reformulated as a Derivative Element andreplaced by the combination of the Prime Data Element and theReconstitution Program. The entry in the distilled data created for thecandidate element is replaced by the Reconstitution Program plus the oneor more references to the relevant Prime Data Elements. If the Distancefor the best derivation exceeds the Distance Threshold, the derivativewill not be accepted.

In order to yield data reduction, the Distance Threshold must always beless than the size of the candidate element. For example, the DistanceThreshold may be set to 50% of the size of the candidate element, sothat a derivative will only be accepted if its footprint is less than orequal to half the footprint of the candidate element, thereby ensuring areduction of 2× or greater for each candidate element for which asuitable derivation exists. The Distance Threshold can be apredetermined percentage or fraction, either based on user-specifiedinput or chosen by the system. The Distance Threshold may be determinedby the system based on static or dynamic parameters of the system.

FIGS. 8A-8E illustrate how data reduction can be performed byfactorizing input data into fixed-sized elements and organizing theelements in a tree data structure that was described in reference toFIGS. 3D and 3E in accordance with some embodiments described herein.FIG. 8A shows how the input data can be simply factorized into 32-bytechunks. Specifically, FIG. 8A shows the first 10 chunks, and then fewmore chunks which appear say 42 million chunks later. FIG. 8B shows theorganization of the Prime Data Elements in the Sieve using Namesconstructed such that the leading bytes of the Name are comprised ofcontent from 3 dimensions in the content of the element (correspondingto locations of an anchor fingerprint, a secondary fingerprint, and atertiary fingerprint). Specifically, in FIG. 8B, each 32 byte chunkbecomes a candidate element of 32 bytes (Fixed-Sized Blocks). A methodof fingerprinting is applied to the content of the element. Each elementhas a Name, which is constructed as follows: bytes of data from threedimensions or fields (located by an anchor fingerprint, a secondaryfingerprint, and a tertiary fingerprint respectively) of the element areconcatenated to form the leading bytes of the Name, followed by the restof the bytes of the element. The Name is used to organize elements inthe Sieve. As shown in FIG. 8B, the first 10 chunks contain noduplicates or derivatives, and are successively installed as elements inthe Sieve. FIG. 8B shows the Sieve after the 10^(th) chunk is consumed.FIG. 8C shows the contents of the Sieve at a subsequent point in timeafter consuming an additional several million elements of data input,e.g., after the next 42 million chunks are presented. The Sieve isexamined for duplicates or derivatives. Chunks that cannot be derivedfrom elements get installed in the Sieve. FIG. 8C shows the Sieve afterthe 42 million chunks are consumed, containing say 16,000,010 elements(logically addressable with 3 bytes of reference address), with theremaining 26,000,000 chunks becoming derivatives. FIG. 8D shows anexample of fresh input that is subsequently presented to the Sieve andidentified as a duplicate of an entry (shown as element number 24,789)in the Sieve. In this example, the Sieve identifies element 24,789(chunk 9) as the most suitable element for chunk 42,000,011. The derivefunction determines that the new chunk is an exact duplicate andreplaces it with a reference to element 24,789. The cost to representthe derivative is 3 byte reference vs 35B original, which is 8.57% ofthe original size. FIG. 8D shows a second example of an input (Chunk42,000,012) that is converted into a derivative of an entry (shown aselement number 187,126) in the Sieve. In this example, the Sievedetermines that there are no exact matches. It identifies elements187,125 and 187,126 (chunks 8 & 1) as the most suitable elements. Thenew element is derived from the most suitable element. Derivation vselement 187,125 and derivation vs element 187,126 are illustrated inFIG. 8D. The cost to represent the derivative vs element 187,125 is 39bytes+3 byte reference=42 bytes, which is 120.00% of the original size.The cost to represent the derivative vs element 187,126 is 12 bytes+3byte reference=15 bytes, which is 42.85% of the original size. The bestderivation (vs element 187,126) is chosen. The reconstitution size iscompared to a threshold. For example if the threshold is 50%, thisderivative (42.85%) is accepted. FIG. 8E provides two additionalexamples of data chunks that are derived from Prime Data Elements,including one example where the derivative is actually created byderiving from two Prime Data Elements. In the first example, chunk42,000,013 is presented. The Sieve identifies element 9,299,998 (chunk10) as the most suitable element. Derivation vs element 9,299,998 isshown in FIG. 8E. The cost to represent the derivative is 4 bytes+3 bytereference=7 bytes, which is 20.00% of the original size. Thereconstitution size is compared to a threshold. For example if thethreshold is 50%, this derivative (20.00%) is accepted. In the secondexample, chunk 42,000,014 is presented. In this example, chunk42,000,014 is such that one half of the chunk can be best derived fromelement 9,299,997 while the other half of the chunk can be best derivedfrom element 9,299,998. Hence, a multi-element derivative is created toyield further data reduction. The multi-element derivation is shown inFIG. 8E. Cost to represent this multi-element derivative is 3 bytereference+3 bytes+3 byte reference=9 bytes, which is 25.71% of theoriginal size. The reconstitution size is compared to a threshold, e.g.,if threshold is 50%, this derivative (25.71%) is accepted. Note that thebest outcome from a single element derivative would have been 45.71%.

FIGS. 8A-E illustrate an important advantage of the Data Distillation™system: that it can be effective in performing data reduction whileconsuming and producing fixed-sized blocks. Note that fixed-sized blocksare highly desired in a high-performance storage system. Using the DataDistillation™ apparatus, a large incoming input file comprised ofnumerous blocks of fixed size can be factorized into numerous elementsof fixed size, so that all the Prime Data Elements are of fixed size.The potentially variable-sized Reconstitution Programs for eachderivative element can be packed together and kept in-line in theDistilled Data file, which can subsequently be chunked into fixed-sizedblocks. Thus, for all practical purposes, powerful data reduction can beperformed while consuming and producing fixed-sized blocks in thestorage system.

FIGS. 9A-C illustrate an example of the Data Distillation™ scheme thatwas first shown in FIG. 1C: this scheme employs a separate PrimeReconstitution Program Sieve that can be accessed in acontent-associative manner. Such a structure enables the detection of aderivative that constructs a Reconstitution Program that is alreadypresent in the Prime Reconstitution Program Sieve. Such a derivative canbe reformulated to reference the existing Reconstitution Program. Thisenables the detection of redundancy among Reconstitution Programs. InFIG. 9A, input data is ingested. A method of fingerprinting is appliedto the data, and chunk boundaries are set at the fingerprint positions.The input is factorized into 8 candidate elements as shown (alternatingchunks shown in bold and regular font in FIG. 9A). In FIG. 9B, the 8candidate elements are shown as organized in the Sieve. Each element hasa distinct Name, constructed from the entire content of the element. Inthis example, the element Name is constructed as follows: bytes of datafrom two dimensions or fields (located by an anchor fingerprint and asecondary fingerprint, respectively) are concatenated to form theleading bytes of the Name, followed by the rest of the bytes. The Nameis used to order elements in the Sieve, and also providecontent-associative access to it through a tree structure. FIG. 9B alsoshows a second content-associative structure that contains PrimeReconstitution Programs. FIG. 9C illustrates duplicate reconstitutions.Suppose a 55-byte candidate element (shown in FIG. 9C) that is not aduplicate of any Prime Data Element arrives. Element 3 is selected asthe most suitable element—the first 2 dimensions are the same for PDEs 2and 3, but the rest of the bytes starting with 88a7 match Element 3. Thenew input is derived from Element 3 with a 12-byte ReconstitutionProgram (RP). Encodings are as shown in FIG. 7A. Note that, for thisexample, max element size is 64 bits and all offsets and lengths areencoded as 6-bit values, as opposed to the 5-bit lengths and offsetsshown in FIG. 7A. The Prime Reconstitution Program Sieve is searched andthis new RP is not found. This RP is inserted into the PrimeReconstitution Program Sieve, ordered based on its value. The newelement is reformulated as a reference to Prime Data Element 3 and areference to the newly created Prime Reconstitution Program at reference4 in the Prime Reconstitution Program Sieve. The total storage size forthis derived element is: 3-byte PDE reference, 3-byte RP reference,12-byte RP=18 bytes, which is 31.0% of the size vs. storing it as a PDE.Later, suppose a copy of the 55-byte candidate element arrives. Asbefore, a 12-byte RP is created based on Element 3. The PrimeReconstitution Program Sieve is searched and the RP with Prime RP ID=3,RP reference=4, is found. This candidate element is represented in thesystem as a reference to Prime Data Element 3 and a reference toReconstitution Program 4. The total storage size added for this derivedelement is now: 3-byte PDE reference, 3-byte RP reference=6 bytes, whichis 10.3% of the size vs. storing it as a PDE.

FIG. 10A provides an example of how transformations specified in theReconstitution Program are applied to a Prime Data Element to yield aDerivative Element in accordance with some embodiments described herein.The example shows a Derivative Element specified to be generated fromPrime Data Element numbered 187,126 (this Prime Data Element is alsoshown in the Sieve in FIG. 8C) by applying to it four transformations(an insertion, replacement, deletion, and append) as specified by theReconstitution Program shown. As shown in FIG. 10A, element 187,126 isloaded from the Sieve, and the Reconstitution Program is executed toderive chunk 42,000,012 from element 187,126. FIGS. 10B-10C illustratedata retrieval processes in accordance with some embodiments describedherein. Each data retrieval request essentially takes the form of anElement in the Distilled Data, presented to the retrieval engine in thelosslessly reduced format. The losslessly reduced format for eachElement contains references to the associated Prime Data Element(s) andthe Reconstitution Program. The Retriever of the Data Distillation™apparatus fetches the Prime Data Elements and Reconstitution Program andfurnishes these to the Reconstitutor for reconstitution. After therelevant Prime Data Elements and Reconstitution Program for an Elementof the Distilled Data have been fetched, the Reconstitutor executes theReconstitution Program to generate the Element in its original unreducedform. The effort required by the data retrieval process to execute thereconstitution is linear with respect to the size of the ReconstitutionProgram and the size of the Prime Data Elements. Hence, high dataretrieval rates can be achieved by the system.

It is evident that to reconstitute an Element from the losslesslyreduced form in the Distilled Data to its original unreduced form, onlythe Prime Data Element(s) and Reconstitution Program specified for theElement need to be fetched. Thus, to reconstitute a given Element, noother Elements need to be accessed or reconstituted. This makes the DataDistillation™ apparatus efficient even when servicing a random sequenceof requests for reconstitution and retrieval. Note that traditionalmethods of compression such as the Lempel Ziv method need to fetch anddecompress the entire window of data containing a desired block. Forexample, if a storage system employs the Lempel-Ziv method to compress 4KB blocks of data using a window of 32 KB, then to fetch and decompressa given 4 KB block, the entire window of 32 KB needs to be fetched anddecompressed. This imposes a performance penalty because more bandwidthis consumed and more data needs to be decompressed in order to deliverthe desired data. The Data Distillation™ apparatus does not incur such apenalty.

The Data Distillation™ apparatus can be integrated into computer systemsin a variety of ways to organize and store data in a manner thatefficiently uncovers and exploits redundancy globally across the entiredata in the system. FIGS. 11A-11G illustrate systems that include a DataDistillation™ mechanism (which can be implemented using software,hardware, or a combination thereof) in accordance with some embodimentsdescribed herein. FIG. 11A presents a general purpose computing platformwith software applications running on system software executing on ahardware platform comprised of processors, memory and data storagecomponents. FIG. 11B shows the Data Distillation™ apparatus integratedinto the application layer of the platform, with each specificapplication using the apparatus to exploit redundancy within the datasetfor that application. FIG. 11C shows the Data Distillation™ apparatusemployed to provide a data virtualization layer or service for allapplications running above it. FIGS. 11D and 11E show two differentforms of integration of the Data Distillation™ apparatus with theoperating system, file system and data management services of the samplecomputing platform. Other methods of integration include (but are notlimited to) integration with an embedded computing stack in the hardwareplatform such as that employed in a flash-based data storage subsystemas shown in FIG. 11F.

FIG. 11G presents additional details of the integration of the DataDistillation™ apparatus with the sample computing platform shown in FIG.11D. FIG. 11G shows the components of the Data Distillation™ apparatus,with the Parser & Factorizer, Deriver, Retriever, and Reconstitutorexecuting as software on the general purpose processor, and thecontent-associative mapping structure residing across a few levels ofthe storage hierarchy. The Prime Data Sieve can reside in the storagemedia (such as flash-based storage drives).

FIG. 11H shows how the Data Distillation™ apparatus may interface withthe sample general purpose computing platform.

A file system (or filesystem) associates a file (e.g., a text document,a spreadsheet, an executable, a multimedia file, etc.) with anidentifier (e.g., a filename, a file handle, etc.), and enablesoperations (e.g., read, write, insert, append, delete, etc.) to beperformed on the file by using the identifier associated with the file.The namespace implemented by a file system can be flat or hierarchical.Additionally, the namespace can be layered, e.g., a top-layer identifiermay be resolved into one or more identifiers at successively lowerlayers until the top-layer identifier is completely resolved. In thismanner, a file system provides an abstraction of the physical datastorage device(s) and/or storage media (e.g., computer memories, flashdrives, disk drives, network storage devices, CD-ROMs, DVDs, etc.) thatphysically store the contents of the file.

The physical storage devices and/or storage media that are used forstoring information in a file system may use one or multiple storagetechnologies, and can be located at the same network location or can bedistributed across different network locations. Given an identifierassociated with a file and one or more operation(s) that are requestedto be performed on the file, a file system can (1) identify one or morephysical storage devices and/or storage media, and (2) cause thephysical storage devices and/or storage media that were identified bythe file system to effectuate the operation that was requested to beperformed on the file associated with the identifier.

Whenever a read or a write operation is performed in the system,different software and/or hardware components may be involved. The term“Reader” can refer to a collection of software and/or hardwarecomponents in a system that are involved when a given read operation isperformed in the system, and the term “Writer” can refer to a collectionof software and/or hardware components in a system that are involvedwhen a given write operation is performed in the system. Someembodiments of the methods and apparatuses for data reduction describedherein can be utilized by or incorporated into one or more softwareand/or hardware components of a system that are involved when a givenread or write operation is performed. Different Readers and Writers mayutilize or incorporate different data reduction implementations.However, each Writer that utilizes or incorporates a particular datareduction implementation will correspond to a Reader that also utilizesor incorporates the same data reduction implementation. Note that someread and write operations that are performed in the system may notutilize or incorporate the data reduction apparatus. For example, whenData Distillation™ Apparatus or Data Reduction Apparatus 103 retrievesPrime Data Elements or adds new Prime Data Elements to the Prime DataStore, it can perform the read and write operations directly withoutdata reduction.

Specifically, in FIG. 11H, Writer 150W can generally refer to a softwareand/or hardware component of a system that is involved when a givenwrite operation is performed, and Reader 150R can generally refer to asoftware and/or hardware component of a system that is involved when agiven read operation is performed. As shown in FIG. 11H, Writer 150Wprovides input data to the Data Distillation™ Apparatus or DataReduction Apparatus 103, and receives Distilled Data 108 from DataDistillation™ Apparatus or Data Reduction Apparatus 103. Reader 150Rprovides retrieval requests 109 to Data Distillation™ Apparatus or DataReduction Apparatus 103, and receives Retrieved Data Output 113 fromData Distillation™ Apparatus or Data Reduction Apparatus 103.

Implementation examples for FIG. 11H include, but are not limited to,incorporating or utilizing the Data Distillation™ Apparatus or DataReduction Apparatus 103 in an application, operating system kernel, filesystem, data management module, device driver, or firmware of a flash ordisk drive. This spans the variety of configurations and usagesdescribed in FIGS. 11B-F.

FIG. 11I illustrates how the Data Distillation™ apparatus may be usedfor data reduction in a block processing storage system. In such a blockprocessing system, data is stored in blocks, and each block isidentified by a Logical Block Address or LBA. Blocks are continuouslybeing modified and overwritten so that fresh data may be overwritteninto a block identified by a particular LBA. Each block in the system istreated as a candidate element and the Data Distillation™ apparatus maybe used to reduce the Candidate Element into the losslessly reduced formcomprising of a reference to a Prime Data Element (stored in aparticular Prime Data Element Block) and in the case of a DerivativeElement a reference to a Reconstitution program (stored in a particularReconstitution Program Block). FIG. 11I introduces a data structure 1151that maps the content of the block identified by an LBA to acorresponding Element in losslessly reduced form. Against each LBA willreside the specification of the associated Element. For a systememploying fixed sized blocks, it is convenient to have the incomingblocks, the Prime Data Element Blocks 1152, and also ReconstitutionProgram Blocks 1153 to all be of fixed size. In this system, each PrimeData Element may be stored as an individual block. MultipleReconstitution Programs may be packed into a Reconstitution ProgramBlock which is also of the same fixed size. The data structure alsocontains a reference to the Count field and associated metadata residingat the Leaf Node Data structure for each of the Prime Data Elements andthe Reconstitution Programs, so that when the block is overwritten withfresh data, the previous data residing at the LBA can be effectivelymanaged—the count field for the existing Prime Data Element andReconstitution Program (that is being overwritten) has to bedecremented, and likewise the Count for a Prime Data Element that isreferenced by incoming data into the LBA has to be incremented. Bymaintaining the reference to the Count field in this data structure1151, overwrites can be speedily managed, thus enabling a highperformance block processing storage system that takes full advantage ofthe data reduction offered by the Data Distillation™ apparatus.

FIG. 12A shows the use of the Data Distillation™ apparatus for thecommunication of data across a bandwidth-constrained communicationmedium in accordance with some embodiments described herein. In thesetup shown, Communication Node A creates a set of files to be sent overto Communication Node B. Node A employs the Data Distillation™ apparatusto transform the input files into distilled data or Distilled Files,containing references to Prime Data Elements installed in a Prime DataSieve, as well as Reconstitution Programs for derivative elements. NodeA then sends the Distilled Files along with the Prime Data Sieve to NodeB (the Prime Data Sieve can be sent prior to, concurrently, or aftersending the Distilled Files; moreover, the Prime Data Sieve may be sentover the same communication channel or over a different communicationchannel than the communication channel that is used for sending theDistilled Files). Node B installs the Prime Data Sieve in acorresponding structure at its end, and subsequently feeds the DistilledFiles through the Retriever and Reconstitutor that are resident in NodeB's Data Distillation™ apparatus to yield the original set of files thatwere created by Node A. Thus, a more efficient use is made of thebandwidth-constrained communication medium, by employing the DataDistillation™ apparatus at both ends of the medium to send only thereduced data. Note that using Data Distillation™ enables exploitingredundancy across a larger scope (beyond what is viable usingconventional techniques, such as Lempel-Ziv) so that even large files orgroups of files can be transmitted efficiently.

We now discuss the use of the Data Distillation™ apparatus in Wide AreaNetwork installations where workgroups collaboratively share data thatis spread across multiple nodes. When data is first created, it can bereduced and communicated as illustrated in FIG. 12A. Wide Area Networksmaintain copies of the data at each site to enable fast local access tothe data. Use of the Data Distillation™ apparatus can reduce thefootprint at each site. Furthermore, upon subsequent ingestion of freshdata at any of the sites, any redundancy between the fresh data and thecontents of the pre-existing Prime Data Sieve can be exploited to reducethe fresh data.

In such an installation, any modifications to the data at any given siteneed to be communicated to all other sites, so that the Prime Data Sieveat each site is kept consistent. Hence, as shown in FIG. 12B, updatessuch as installations and deletions of Prime Data Elements, as well asmetadata updates, can be communicated to the Prime Data Sieve at eachsite in accordance with some embodiments described herein. For example,upon installing a fresh Prime Data Element into the Sieve at a givensite, the Prime Data Element needs to be communicated to all othersites. Each site can access the Sieve in a content associative mannerusing the value of the Prime Data Element and determine where in theSieve the new entry needs to be added. Likewise, upon deleting a PrimeData Element from the Sieve at a given site, all other sites need to beupdated to reflect the deletion. One way this could be accomplished isby communicating the Prime Data Element to all sites so that each sitecan content-associatively access the Sieve using the Prime Data Elementto determine which entry in the leaf node needs to be deleted, alongwith necessary updates to the related links in the tree as well asdeletion of that Prime Data Element from the Sieve. Another method is tocommunicate to all sites a reference to the entry for the Prime DataElement in the leaf node where the Prime Data Element resides.

Thus, the Data Distillation™ apparatus can be used to reduce thefootprint of data stored across the various sites of a Wide Area Networkas well as make efficient use of the communication links of the network.

FIGS. 12C-12K illustrate the various components of the reduced dataproduced by the Data Distillation™ apparatus for various usage models inaccordance with some embodiments described herein.

FIG. 12C illustrates how the Data Distillation™ apparatus 1203 ingests aset of Input Files 1201 and after completion of the distillation processgenerates a set of Distilled Files 1205 and a Prime Data Sieve or PrimeData Store 1206. The Prime Data Sieve or Prime Data Store 1206 of FIG.12C itself is comprised of two components, viz. Mapper 1207 and thePrime Data Elements (or PDEs) 1208 as shown in FIG. 12D.

Mapper 1207 itself has two components within it, namely, the set of treenode data structures and the set of leaf node data structures thatdefine the overall tree. The set of tree node data structures could beplaced into one or more files. Likewise the set of leaf node datastructures could be placed into one or more files. In some embodiments,a single file called the Tree Nodes File holds the entire set of treenode data structures for the tree created for the Prime Data Elementsfor the given dataset (Input Files 1201), and another single file calledthe Leaf Nodes File holds the entire set of leaf node data structuresfor the tree created for the Prime Data Elements for that dataset.

In FIG. 12D, Prime Data Elements 1208 contains the set of Prime DataElements created for the given dataset (Input Files 1201). The set ofPrime Data Elements could be placed into one or more files. In someembodiments, a single file called the PDE File holds the entire set ofPrime Data Elements created for the given dataset.

The tree nodes in the Tree Nodes File will contain references to othertree nodes within the Tree Nodes File. The deepest layer (or lowermostlevels) of tree nodes in the Tree Nodes File will contain references toentries in leaf node data structures in the Leaf Nodes File. Entries inthe leaf node data structures in the Leaf Nodes File will containreferences to Prime Data Elements in the PDE File.

The Tree Nodes File, Leaf Nodes File and PDE File are illustrated inFIG. 12E which shows details of all the components created by theapparatus. FIG. 12E shows a set of Input Files 1201 comprising of Nfiles named file1, file2, file3, . . . fileN that get reduced by theData Distillation™ apparatus to produce a set of Distilled Files 1205and the various components of the Prime Data Sieve, viz., Tree NodesFile 1209, Leaf Nodes File 1210, and PDE File 1211. Distilled Files 1205comprises of N files named file1.dist, file2.dist, file3.dist . . .fileN.dist. The Data Distillation™ apparatus factorizes the input datainto its constituent elements and creates two categories of dataelements—Prime Data Elements and Derivative Elements. The DistilledFiles contain descriptions of the data elements in the losslesslyreduced format and contain references to Prime Data Elements in the PDEFile. Each file in Input Files 1201 has a corresponding distilled filein Distilled Files 1205. For example, file1 1212 in Input Files 1201corresponds to the distilled file named file1.dist 1213 in DistilledFiles 1205.

Note that FIG. 12E shows the various components created by the DataDistillation Apparatus based on an organization of the Distilled Dataand the Prime Data Sieve in accordance with FIG. 1A, whereReconstitution Programs are placed in the losslessly reducedrepresentation of the Element in the Distilled File. Note that someembodiments (in accordance with FIG. 1B) can place the ReconstitutionPrograms in the Prime Data Sieve and treat them just like Prime DataElements. The losslessly reduced representation of the Element in theDistilled File will contain a reference to the Reconstitution Program inthe Prime Data Sieve (rather than contain the Reconstitution Programitself). In these embodiments, the Reconstitution Programs will betreated like Prime Data Elements and be produced in the PDE File 1211.In yet another embodiment, in accordance with FIG. 1C, theReconstitution Programs are stored separate from the Prime Data Elementsin a structure called the Reconstitution Program Store. In suchembodiments, the losslessly reduced representation of the Element in theDistilled File will contain a reference to the Reconstitution Program inthe Reconstitution Program Store. In such embodiments, as illustrated inFIG. 12F, in addition to producing the Tree Nodes File 1209, Leaf NodesFile 1210 and PDE File 1211 for the tree organization of the Prime DataElements, the apparatus will also produce a second set of tree and leafnode files referred to as Recon Tree Nodes File 1219 and Recon LeafNodes File 1220, along with a file containing all the ReconstitutionPrograms referred to as the RP File 1221.

The Data Distillation™ apparatus shown in FIG. 12E also storesconfiguration and control information governing its operation in one ormore of the Tree Nodes File 1209, Leaf Nodes File 1210, PDE File 1211and Distilled Files 1205. Alternatively, a fifth component containingthis information may be generated. Similarly for the apparatus shown inFIG. 12F, the configuration and control information could be stored inone or more of the various components shown in FIG. 12F, or it could bestored in another component generated for this purpose.

FIG. 12G illustrates an overview of the usage of the Data Distillation™apparatus, where a given dataset (Input Dataset 1221) is fed to the DataDistillation™ apparatus 1203 and processed to produce a losslesslyreduced dataset (Losslessly Reduced Dataset 1224). Input Dataset 1221could be comprised of a collection of files, objects, blocks, chunks, orextracts from a data stream. Note that FIG. 12E illustrates the examplewhere the dataset is comprised of files. Input Dataset 1221 of FIG. 12Gcorresponds to Input Files 1201 of FIG. 12E, while Losslessly ReducedDataset 1224 of FIG. 12G includes four components shown in FIG. 12E,namely Distilled Files 1205, Tree Nodes File 1209, Leaf Nodes File 1210,and PDE File 1211 of FIG. 12E. In FIG. 12G, the Data Distillation™apparatus exploits redundancy among data elements across the entirescope of the Input Dataset that is presented to it.

The Data Distillation™ apparatus can be configured to exploit redundancyacross a subset of the Input Dataset and deliver lossless reduction foreach subset of data presented to it. For example, as shown in FIG. 12H,Input Dataset 1221 can be partitioned into numerous smaller collectionsof data, each collection being referred to in this disclosure as a “lot”or a “Lot of Data” or a “Data Lot”. FIG. 12H shows the DataDistillation™ apparatus configured to ingest Input Data Lot 1224 andproduce Losslessly Reduced Data Lot 1225. FIG. 12H shows Input Dataset1221 comprised of a number of collections of data which are Data Lot 1,. . . Data Lot i, . . . Data Lot n. The data is presented to the DataDistillation™ apparatus one Data Lot at a time, and redundancy isexploited across the scope of each Data Lot to generate a LosslesslyReduced Data Lot. For example, Data Lot i 1226 from Input Dataset 1221is fed to the apparatus and Losslessly Reduced Data Lot i 1228 isdelivered to Losslessly Reduced Dataset 1227. Each Data Lot from InputDataset 1221 is fed to the apparatus and the corresponding LosslesslyReduced Data Lot is delivered to the Losslessly Reduced Dataset 1227.Upon consuming and reducing all of Data Lot 1, . . . Data Lot i . . .Data Lot n, Input Dataset 1221 is reduced to Losslessly Reduced Dataset1227.

While the Data Distillation™ apparatus is by design already efficient atexploiting redundancy across the global scope of data, the abovetechnique may be used to further speed up the data reduction process andfurther improve its efficiency. The throughput of the data reductionprocess can be increased by limiting the size of a Data Lot to be ableto fit into the available memory of a system. For example, an InputDataset which is many terabytes or even petabytes in size could bebroken up into numerous Data Lots each of size say 256 GB, and each DataLot can be speedily reduced. Using a single processor core (Intel XeonE5-1650 V3, Haswell 3.5 Ghz processor) with 256 GB of memory, such asolution exploiting redundancy across a scope of 256 GB has beenimplemented in our labs to yield ingest rates of several hundredmegabytes per second of data while delivering reduction levels of 2-3×on various datasets. Note that a scope of 256 GB is many million-foldlarger than 32 KB, which is the size of the window at which the LempelZiv method delivers ingest performance of between 10 MB/sec to 200MB/sec on modern processors. Thus, by limiting the scope of redundancyappropriately, improvements in the speed of the data distillationprocess can be achieved by potentially sacrificing some reduction.

FIG. 12I illustrates a variation of the setup in FIG. 12H, and showsmultiple data distillation processes running on multiple processors tosignificantly boost the throughput of data reduction (and also datareconstitution/retrieval) of the input dataset. FIG. 12I shows the InputDataset 1201 partitioned into x number of Data Lots, and the xindependent Data Lots are fed into the j independent processes runningon independent processor cores (with each process being allocatedsufficient memory to accommodate any Data Lot that will be fed to it) toget executed in parallel and yield approximately j-fold speedup for bothdata reduction as well as reconstitution/retrieval. FIG. 12J illustratesthe various components of the reduced data produced by the DataDistillation™ apparatus for a usage model where the mapper is no longerneeded to be retained subsequent to reduction of the Input Dataset.Examples of such usage models are certain kinds of data backup and dataarchiving applications. In such a usage model, the only subsequent useof the reduced data is reconstitution and retrieval of the Input Datasetfrom the Reduced Dataset. In such a scenario, the footprint of theReduced Data can be further reduced by not storing the Mapper after thedata reduction is completed. FIG. 12J shows Input Files 1201 fed to theapparatus, which produces Distilled Files 1205 and PDE File 1211—thesecomponents comprise the Reduced Data in this scenario. Note that theInput Files 1201 can be completely regenerated and recovered usingDistilled Files 1205 and PDE File 1211 only. Recall that the losslesslyreduced representation for each element in the Distilled Files containsthe Reconstitution Program where needed, as well as references to PrimeData Elements in the PDE File. Coupled with the PDE File, this is allthe information needed to execute reconstitution.

Note that FIG. 12J shows the various components created by the DataDistillation Apparatus based on an organization of the Distilled Dataand the Prime Data Sieve in accordance with FIG. 1A, whereReconstitution Programs are placed in the losslessly reducedrepresentation of the Element in the Distilled File. Note that someembodiments (in accordance with FIG. 1B) can place the ReconstitutionPrograms in the Prime Data Sieve and treat them just like Prime DataElements. The losslessly reduced representation of the Element in theDistilled File will contain a reference to the Reconstitution Program inthe Prime Data Sieve (rather than contain the Reconstitution Programitself). In these embodiments, the Reconstitution Programs will betreated like Prime Data Elements and be produced in the PDE File 1211.In yet another embodiment, in accordance with FIG. 1C, theReconstitution Programs are stored separate from the Prime Data Elementsin a structure called the Reconstitution Program Store. In suchembodiments, the losslessly reduced representation of the Element in theDistilled File will contain a reference to the Reconstitution Program inthe Reconstitution Program Store. In such embodiments, in addition toproducing the PDE file for the Prime Data Elements, the apparatus willalso produce a file containing all the Reconstitution Programs referredto as the RP File. This is shown in FIG. 12K, which shows the componentsof the reduced data for usage models where the mappers no longer need tobe retained. FIG. 12K shows the reduced data components comprising theDistilled Files 1205, PDE File 1211, and RP File 1221.

FIGS. 12L-P illustrate how the Distillation process can be deployed andexecuted on distributed systems to be able to accommodate very largedatasets at very high ingest rates in accordance with some embodimentsdescribed herein.

The distributed computing paradigm entails distributed processing oflarge datasets by programs running on multiple computers. FIG. 12L showsa number of computers networked together in an organization referred toas a distributed computing cluster. FIG. 12L shows point-to-point linksbetween the computers, but it will be understood that any communicationtopology, e.g., hub-and-spoke topology or mesh topology, can be used inplace of the topology shown in FIG. 12L. In a given cluster, one node isappointed as the master node which distributes tasks to the slave nodesand controls and co-ordinates their overall operation. Slave nodesexecute tasks as directed by the master.

The Data Distillation Process can be executed in a distributed fashionacross the multiple nodes of a distributed computing cluster to harnessthe total compute, memory, and storage capacity of the numerouscomputers in the cluster. In this setup, a master distillation module onthe master node interacts with slave distillation modules running onslave nodes to achieve the data distillation in a distributed fashion.To facilitate this distribution, the Prime Data Sieve of the apparatuscan be partitioned into multiple independent subsets or subtrees thatcan be distributed across multiple slave modules running on the slavenodes. Recall that in the Data Distillation Apparatus, the Prime DataElements are organized in tree form based upon their Names, and theirNames are derived from their content. The Prime Data Sieve can bepartitioned into multiple independent subsets or Child Sieves based onthe leading bytes of the Name of Elements in the Prime Data Sieve. Therecan be multiple ways to partition the Name space across multiplesubtrees. For example, the values of the leading bytes of the Name ofelements can be partitioned into a number of subranges, and eachsubrange assigned to a Child Sieve. There can be as many subsets orpartitions created as there are slave modules in the cluster, so eachindependent partition is deployed on a particular slave module. Usingthe deployed Child Sieve, each slave module is designed to execute thedata distillation process on candidate elements that it receives.

FIG. 12M illustrates a sample partition of the Prime Data Sieve into 4Prime Data Sieves or Child Sieves labelled PDS_1, PDS_2, PDS_3, andPDS_4 which will be deployed on 4 slave modules running on 4 nodes. Thepartitioning is based on the leading byte of the Names of Prime DataElements. In the example shown, the leading byte of the Name of allelements in PDS_1 will be in the range A through I and the Sieve PDS_1will have a Name A_I marked by the range of values that steer to it.Likewise, the leading byte of the Name of all elements in PDS_2 will bein the range J through O and the Child Sieve PDS_2 will have a Name J_Omarked by the range of values that steer to it. Likewise, the leadingbyte of the Name of all elements in PDS_3 will be in the range P throughS and the Child Sieve PDS_3 will have a Name P_S marked by the range ofvalues that steer to it. Lastly, the leading byte of the Name of allelements in PDS_4 will be in the range T through Z and the Child SievePDS_4 will have a Name T_Z marked by the range of values that steer toit.

In this setup, the master module running on the master node receives anInput File and performs a lightweight parsing and factorization of theInput File to break the Input File into a sequence of candidateelements, and subsequently steer each candidate element to a suitableslave module for further processing. The lightweight parsing mightinclude parsing each candidate element against a schema, or mightinclude the application of fingerprinting on the candidate element todetermine the dimensions that constitute the leading bytes of the Nameof the candidate element. The parsing at the master is limited toidentify only as many bytes as is sufficient to determine which slavemodule should receive the candidate element. Based upon the value in theleading bytes of the Name of the candidate element, the candidate isforwarded to the slave module at the slave node which holds theChild-Sieve that corresponds to this specific value.

As data accumulates into the Sieve, the partition can be intermittentlyrevisited and rebalanced. The partitioning and rebalancing functions canbe performed by the master module.

Upon receiving a candidate element, each slave module executes the DataDistillation process, starting with a complete parsing and examinationof the candidate element to create its Name. Using this Name, the slavemodule performs a content associative lookup of the Child Sieve, andexecutes the distillation process to convert the candidate element intoan Element in the losslessly reduced representation with respect to thatChild Sieve. The losslessly reduced representation of an Element in theDistilled File is enhanced with a field called SlaveNumber to identifythe slave module and corresponding Child Sieve with respect to which theElement has been reduced. The losslessly reduced representation of theElement is sent back to the master module. If the candidate element isnot found in the Child Sieve or cannot be derived from Prime DataElements in the Child Sieve, a fresh Prime Data Element is identified tobe allocated into the Child Sieve.

The master module continues to steer all candidate elements from anInput File to appropriate slave modules and accumulates the incomingElement descriptions (in losslessly reduced representation) until it hasreceived all Elements for the Input File. At that point a global commitcommunication can be issued to all slave modules to update theirrespective Child Sieves with the outcome of their individualdistillation processes. The Distilled File for the input is stored atthe master module.

In some embodiments, rather than wait for the entire Distilled File tobe prepared before any slave can update its Child Sieve with eitherfresh Prime Data Elements or metadata, the updates to the Child Sievesmay be completed as the candidate elements get processed at the slavemodules.

In some embodiments, each Child Sieve contains Prime Data Elements aswell as Reconstitution Programs in accordance with the descriptions forFIGS. 1B and 1C. In such embodiments, the Reconstitution Program isstored in the Child Sieve and the losslessly reduced representationcontains references to both Prime Data Elements as well asReconstitution Programs (where needed) in the Child Sieve. This furtherreduces the size of the Element and hence the size of the Distilled Filewhich needs to be stored at the master module. In some embodiments, thePrime Reconstitution Program Sieve in each Child Sieve contains thoseReconstitution Programs that are used to create Derivations off PrimeData Elements resident in that Child Sieve. In such a case, the PrimeReconstitution Programs are available locally at the Slave Node andenable rapid derivation and reconstitution without any delay that wouldotherwise be incurred to fetch the Prime Reconstitution Program from aremote node. In other embodiments, the Prime Reconstitution ProgramSieve is distributed globally across all the nodes to take advantage ofthe total capacity of the distributed system. The losslessly reducedrepresentation is enhanced with a second field that identifies the slavenode or Child Sieve that contains the Prime Reconstitution Program. Insuch an embodiment, the solution incurs an additional delay to fetch thePrime Reconstitution Program from a remote node in order to eithergenerate the final Reconstitution Program through derivation, or toreconstitute the Element. The overall method takes advantage of thecombined storage capacity of all the slave nodes to distribute filesacross all the nodes, based upon the content of each chunk or candidateelement in each file.

Data retrieval is similarly co-ordinated by the master module. Themaster module receives a Distilled File and examines the losslesslyreduced specification for each Element in the Distilled File. Itextracts the field “SlaveNumber” that indicates which slave module willreconstitute the Element. The Element is then sent to the appropriateslave module for reconstitution. The Reconstituted Element is then sentback to the master module. The master module assembles ReconstitutedElements from all the slaves and forwards the Reconstituted file to theconsumer that is demanding the file.

FIG. 12N illustrates how the Data Distillation apparatus may be deployedand executed in distributed systems. Input File 1251 is fed to themaster module which parses and identified the leading bytes of the Nameof each candidate element in the file. The master module steerscandidate elements to one of 4 slave modules. Slave Module1 at SlaveNode 1 which holds PDS 1 or Child Sieve with Name A_I containing PrimeData Elements with leading byte of Name bearing values in the range Athrough I receives Candidate Element 1252 with Name BCD . . . which isdetermined to be a duplicate of an element already present in ChildSieve with Name A_I. Slave Module 1 returns the Losslessly ReducedRepresentation 1253 which contains the indicator that the Element isprime, and residing in Slave1 at address refPDE1. The master sends allcandidate elements to the relevant slave modules as shown in FIG. 12Nand assembles and collects and finally stores the Distilled File.

FIG. 12O illustrates a variation of the scheme shown in FIG. 12N. Inthis variation, in the losslessly reduced representation of each elementin the distilled file, the field which identifies the particularChild_Sieve with respect to which the element has been reduced containsthe Name of that Child_Sieve instead of the number of the module or nodeon which that Child_Sieve resides. Hence, the field SlaveNumber isreplaced by the field Child_Sieve_Name. This has the benefit ofreferring to the relevant Child_Sieve by its virtual address rather thanthe number of the module or the physical node where the Child_Sieveresides. Thus, as can be seen in FIG. 12O, Slave Module1 at Slave Node 1which holds PDS_1 or Child_Sieve with Name A_I containing Prime DataElements with leading byte of Name bearing values in the range A throughI receives Candidate Element 1252 with Name BCD . . . which isdetermined to be a duplicate of an element already present inChild_Sieve with Name A_I. Slave Module 1 returns the Losslessly ReducedRepresentation 1254 which contains the indicator that the Element isprime, and residing in Child_Sieve with Name A_I at address refPDE1.

Note that by employing the arrangements described in FIGS. 12L through12O, the overall throughput rate of the data distillation process can beincreased. The throughput at the master will now be limited bylightweight parsing and dispatch of candidate elements from the mastermodule. Distillation for numerous candidate elements will execute inparallel, so long as their content steers them to distinct slavemodules.

To further boost the overall throughput, the task of lightweight parsingand factorization of the input stream to identify which Child_Sieveshould receive the candidate element can be parallelized. This task canbe partitioned by the master module into multiple concurrent tasks to beexecuted in parallel by the slave modules running on the multiple slavenodes. This can be accomplished by looking ahead in the data stream andslicing the data stream into multiple partially overlapping segments.These segments are sent by the master to each of the slave modules whichperform the lightweight parsing and factorization in parallel and sendback the results of the factorization to the master. The master resolvesthe factorization across the boundaries of each of the segments and thenroutes the candidate elements to the appropriate slave module.

FIGS. 12L through 12O described an arrangement where the datadistillation apparatus operates in a distributed fashion with a masterdistillation module running on a master node and multiple slavedistillation modules running on slave nodes. The master module wasresponsible for performing the partitioning of Prime Data Elementsacross the various Child Sieves. In the arrangement shown, all InputFiles to be ingested were ingested by the master module and losslesslyreduced Distilled Files were retained at the master module, while allPrime Data Elements (and any Prime Reconstitution Programs) resided inChild Sieves at the various slaves. Data retrieval requests for a Filewere also processed by the master, and the reconstitution of thecorresponding Distilled Files was coordinated by the master. FIG. 12Pillustrates a variation where Input Files can be ingested by any of theslave distillation modules (and the corresponding Distilled Filesretained at those modules), and data retrieval requests can be processedby any of the slave distillation modules. The master module continues toperform the partitioning of the Prime Data Elements across the ChildSieves in the same manner, so that the distribution of Prime DataElements across the Child Sieves would be the same as in thearrangements shown in FIGS. 12L through 12O. However, in the newarrangement shown in FIG. 12P, each slave module is made aware of thepartitioning, since each slave module can both ingest and retrieve data.Additionally, all modules are made aware of the existence and locationof Distilled Files created and stored at each of the modules uponingestion of data by those modules. This allows any slave module tosatisfy data retrieval requests for any of the Files stored in theentire system.

As shown in FIG. 12P, each of the slave modules can ingest and retrievedata from the distributed storage system. For example Slave DistillationModule 1 1270 ingests Input File I 1271 and performs lightweight parsingto factorize the Input File I and route candidate elements to the modulecontaining the Child_Sieve that corresponds to the name of eachcandidate element from Input File I. For example, candidate element 1275from Input File I is sent to Slave Distillation Module 2 1279. Likewise,Slave Distillation Module 2 1279 ingests Input File II and performslightweight parsing to factorize the Input File II and route candidateelements to the module containing the Child_Sieve that corresponds tothe name of each candidate element from Input File II. For example,candidate element 1277 from Input File II is sent to Slave DistillationModule 1 1270. Each of the Slave Distillation Modules process thecandidate elements that they receive, complete the distillation processwith respect to their Child_Sieve, and return the losslessly reducedrepresentation of the candidate element back to the initiating modulethat ingested the data. For example, in response to receiving candidateelement 1275 from Input File I from Slave Distillation module 1 1270,Slave Distillation Module 2 1279 returns losslessly reduced element 1276to Slave Distillation Module 1 1270. Likewise, in response to receivingcandidate element 1277 from Input File II from Slave Distillation module2 1279, Slave Distillation Module 1 1270 returns losslessly reducedelement 1278 to Slave Distillation Module 2 1279.

In this arrangement, retrieval of data can be satisfied at any slavemodule. The module that receives the retrieval request needs to firstdetermine where the Distilled File for that requested File resides, andfetch the Distilled File from the corresponding slave module.Subsequently, the initiating slave module needs to co-ordinate thedistributed reconstitution of the various elements in that DistilledFile to yield the original File and deliver it to the requestingapplication.

In this fashion, the Data Distillation Process can be executed in adistributed manner across multiple nodes of a distributed system to moreeffectively harness the total compute, memory, and storage capacity ofthe numerous computers in the cluster. All nodes in the system can beutilized to ingest and retrieve data. This should enable very high ratesof data ingestion and retrieval while taking full advantage of the totalcombined storage capacity of the nodes in the system. This also allowsapplications running on any node in the system to make a query at alocal node for any data stored anywhere in the system, and to have thatquery satisfied efficiently and seamlessly.

In the arrangements described in FIGS. 12M through 12P, the partitioningof data across Child Sieves resident in the various nodes of the systemwas based upon the Name of Elements in a globally visible name space,where the Elements were extracted by factorizing the input Files. In analternate arrangement, a Data Lot or an entire group of Files that sharecertain metadata can be assigned and stored on a particular Node. Thusthe primary partitioning of the overall data is based on Data Lots, andis performed and managed by the master. All Slave Modules are kept awareof the allocation of Data Lots to Modules. A Data Lot will resideentirely on a given Slave Node. The Child_Sieve on the DistillationSlave Module running on that Slave Node will contain all Prime DataElements belonging to this Data Lot. In other words, the entire tree forall Prime Data Elements for a given Data Lot will reside completely on asingle Child_Sieve within a single Slave Distillation Module. AllDistilled Files for a given Data Lot will also reside on the same SlaveDistillation Module. Using this arrangement, Input Files can still beingested by any of the slave distillation modules, and data retrievalrequests can still be processed by any of the slave distillationmodules. However, the entire data distillation process for a given DataLot executes completely on the Module containing that Data Lot. Requestsfor data ingestion and data retrieval are routed from the initiatingmodules to the particular slave module that is designated to hold theparticular Data Lot. This solution has the benefit of reducedcommunication overhead in the distributed environment when factorizingand distilling a Data Lot. Redundancy is no longer exploited across theentire global data footprint, but very efficiently exploited locallywithin the Data Lot. The solution still uses the combined storagecapacity of the distributed system and offers seamless ability to query,ingest and retrieve any data from any node of the system.

Thus, employing the numerous techniques described above, an efficientuse is made of the resources in the distributed system to perform datadistillation on very large datasets at very high speeds.

Data reduction was performed on a variety of real world datasets usingthe embodiments described herein to determine the effectiveness of theseembodiments. The real world datasets studied include the Enron Corpus ofcorporate email, various U.S. Government records and documents, U.S.Department of Transportation records entered into the MongoDB NOSQLdatabase, and corporate PowerPoint presentations available to thepublic. Using the embodiments described herein, and factorizing theinput data into variable-sized elements (with boundaries determined byfingerprinting) averaging 4 KB, an average data reduction of 3.23× wasachieved across these datasets. A reduction of 3.23× implies that thesize of the reduced data is equal to the size of the original datadivided by 3.23×, leading to a reduced footprint with a compressionratio of 31%. Traditional data deduplication techniques were found todeliver a data reduction of 1.487× on these datasets using equivalentparameters. Using the embodiments described herein, and factorizing theinput data into fixed-sized elements of 4 KB, an average data reductionof 1.86× was achieved across these datasets. Traditional datadeduplication techniques were found to deliver a data reduction of 1.08×on these datasets using equivalent parameters. Hence, the DataDistillation™ solution was found to deliver significantly better datareduction than traditional data deduplication solutions.

The test runs also confirm that a small subset of the bytes of a PrimeData Element serve to order the majority of the elements in the Sieve,thus enabling a solution that requires minimal incremental storage forits operation.

The results confirm that the Data Distillation™ apparatus efficientlyenables exploiting redundancy among data elements globally across theentire dataset, at a grain that is finer than the element itself. Thelossless data reduction delivered by this method is achieved with aneconomy of data accesses and IOs, employing data structures thatthemselves require minimal incremental storage, and using a fraction ofthe total computational processing power that is available on modernmulticore microprocessors. Embodiments described in the precedingsections feature systems and techniques that perform lossless datareduction on large and extremely large datasets while providing highrates of data ingestion and data retrieval, and that do not suffer fromthe drawbacks and limitations of conventional techniques.

Performing Content Associative Search and Retrieval on Data that hasbeen Losslessly Reduced by Deriving Data from Prime Data ElementsResident in a Prime Data Sieve

The Data Distillation Apparatus described in the preceding text andillustrated in FIGS. 1A through 12P can be enhanced with certainfeatures in order to efficiently perform multidimensional search andcontent associative retrieval of information from the data that isstored in the losslessly reduced format. Such multidimensional searchesand data retrieval are key building blocks for an analytics or datawarehousing application. These enhancements will now be described.

FIG. 13 shows a Leaf Node Data Structure similar to the structureillustrated in FIG. 3H. However, in FIG. 13 , the entry in the leaf nodedata structure for each Prime Data Element is enhanced to containreferences (which will also be called Reverse References or ReverseLinks) to all Elements in the Distilled Data that contain a reference tothat particular Prime Data Element. Recall that the Data Distillationscheme factorizes data from an Input File into a sequence of Elementswhich are placed in the Distilled File in a reduced format using aspecification such as that described in FIG. 1H. There are two kinds ofElements in the Distilled File—Prime Data Elements and DerivativeElements. The specification for each of these Elements in the DistilledFile will contain references to Prime Data Elements resident in thePrime Data Sieve. For each of these references (from Element inDistilled File to Prime Data Element in the Prime Data Sieve) there willbe a corresponding Reverse Link or Reverse Reference (from entry for thePrime Data Element in the Leaf Node Data structure to Element in theDistilled File) installed in the Leaf Node Data Structure. The ReverseReference determines the offset within the Distilled File that marks thestart of the losslessly reduced representation of the Element. In someembodiments, the Reverse Reference comprises the name of the DistilledFile and an offset within that file which locates the start of theElement. As shown in FIG. 13 , along with the Reverse Reference to eachElement in the Distilled File, the leaf node data structure also keepsan indicator which identifies whether the Element being referred to inthe Distilled File is a Prime Data Element (prime) or whether it is aDerivative Element (deriv). During the distillation process, the ReverseLinks are installed into the Leaf Node Data Structures as and whenElements are placed into the Distilled File.

The Reverse Reference or Reverse Link is designed as a universal handlewhich can reach all Elements in all Distilled Files that share the PrimeData Sieve.

The addition of the Reverse References is not expected to significantlyimpact the data reduction achieved, since data element size is expectedto be chosen such that each reference is a fraction of the size of thedata element. For example, consider a system where Derivative Elementsare constrained to each derive off no more than 1 Prime Data Element (somulti-element derivatives are not allowed). The total number of ReverseReferences across all Leaf Node Data Structures will equal the totalnumber of Elements across all Distilled Files. Assume the sample inputdataset of 32 GB size is reduced to 8 GB of losslessly reduced data,employing an average element size of 1 KB, and yielding a reductionratio of 4×. There are 32M elements in the input data. If each ReverseReference is 8B in size, the total space occupied by the ReverseReferences is 256 MB, or 0.25 GB. This is a small increase to the 8 GBfootprint of the reduced data. The new footprint will be 8.25 GB and theeffective reduction achieved will be 3.88×, which represents a loss ofreduction of 3%. This is a small price to pay for the benefits ofpowerful content associative data retrieval on the reduced data.

As described earlier in this document, the Distillation Apparatus canemploy a variety of methods to determine the locations of the variouscomponents of the Skeletal Data Structure within the content of acandidate element. The various components of the Skeletal Data Structureof the element can be considered as Dimensions, so that a concatenationof these Dimensions followed by the rest of the content of each elementis used to create the Name of each element. The Name is used to orderand organize the Prime Data Elements in the tree.

In usage models where the structure of the input data is known, a schemadefines the various fields or Dimensions. Such a schema is furnished bythe Analytics Application that is using this Content Associative DataRetrieval Apparatus and is provided to the apparatus through aninterface to the application. Based upon the declarations in the schema,the Parser of the Distillation Apparatus is able to parse the content ofa candidate element to detect and locate the various Dimensions andcreate the Name of the candidate element. As described earlier, Elementsthat have the same content in the fields corresponding to the Dimensionswill be grouped together along the same leg of the tree. For each PrimeData Element installed into the Sieve, the information on the Dimensionscan be stored as metadata in the entry for that Prime Data Element inthe Leaf Node Data Structure. This information can include thelocations, sizes, and values of content at each of the declaredDimensions and can be stored in the field referred to in FIG. 13 as“Other Metadata for Prime Data Element”.

FIG. 14A illustrates a sample schema that provides a description of thestructure of the input dataset and a description of the correspondencebetween the structure of the input dataset and Dimensions in accordancewith some embodiments described herein. Structure description 1402 is anexcerpt or a portion of a more complete schema that describes thecomplete structure of the input data. Structure description 1402includes a listing of keys (e.g., “PROD_ID,” “MFG,” “MONTH,” “CUS_LOC,”“CATEGORY,” and “PRICE”) followed by the type of value that correspondsto the key. The colon character “:” is used as a delimiter to separatethe key from the type of the value, and the semicolon character “;” isused as a delimiter to separate distinct pairs of keys and thecorresponding type of value. Note that the complete schema (of whichStructure 1402 is a part) may specify additional fields to identify thestart and end of each input, and also possibly other fields outside ofDimensions. Dimension mapping description 1404 describes how theDimensions that are used for organizing Prime Data Elements map to thekey values in the structured input dataset. For example, the first linein Dimension mapping description 1404 specifies that the first fourbytes (because the first line ends with the text “prefix=4”) of thevalue corresponding to the key “MFG” in the input dataset is used tocreate Dimension 1. The remaining lines in Dimension mapping description1404 describe how to create the other three dimensions based on thestructured input data. In this mapping of keys to Dimensions, the orderof the keys as they appear in the input does not necessarily match theorder of the Dimensions. Using the schema descriptions provided, theparser can recognize these Dimensions in the input data to create theName of the candidate element. For the example in FIG. 14A, and usingDimension mapping description 1404, the Name of a candidate element willbe created as follows—(1) the first 4 bytes of the Name will be thefirst 4 bytes from the value corresponding to the key “MFG” which isdeclared as Dimension 1, (2) the next 4 bytes of the Name will be thefirst 4 bytes from the value corresponding to the key “CATEGORY” whichis declared as Dimension 2, (3) the next 3 bytes of the Name will be thefirst 3 bytes from the value corresponding to the key “CUS_LOC” which isdeclared as Dimension 3, (4) the next 3 bytes of the Name will be thefirst 3 bytes from the value corresponding to the key “MONTH” which isdeclared as Dimension 4, (5) the next set of the bytes of the Name willbe comprised of a concatenation of the rest of the bytes from theDimensions, (6) and finally, after all the bytes of the Dimensions areexhausted, the rest of the bytes of the Name will be the created from aconcatenation of the rest of the bytes of the candidate element.

The schemas furnished by the application driving this apparatus mayspecify a number of Primary Dimensions as well as a number of SecondaryDimensions. Information for all of these Primary and SecondaryDimensions can be retained in the metadata in the Leaf Node DataStructure. The Primary Dimensions are used to form the principal axisalong which to sort and organize the elements in the Sieve. If PrimaryDimensions are exhausted and subtrees with large membership stillremain, then Secondary Dimensions may also be used deeper down the treeto further subdivide the elements into smaller groups. Information onthe Secondary Dimensions can be retained as metadata and also used assecondary criteria to differentiate the elements within a leaf node. Insome embodiments offering content associative multidimensional searchand retrieval, a requirement may be placed that all incoming data mustcontain the keys and valid values for each of the Dimensions declared bythe schema. This allows the system a way to ensure that only valid dataenters the desired subtrees in the Sieve. Candidate elements whicheither do not contain all fields specified as Dimensions or whichcontain invalid values in the values corresponding to the fields for theDimensions will be sent down a different subtree as illustrated earlierin FIG. 3E.

The Data Distillation apparatus is constrained in one additional way inorder to comprehensively support content associative search andretrieval of data based upon the content in the Dimensions. WhenDerivative Elements are created off a Prime Data Element, the Deriver isconstrained to ensure that both the Prime Data Element and theDerivative have the exact same content in the value fields for each ofthe corresponding Dimensions. Thus, when a derivative is being created,the Reconstitution Program is not allowed to perturb or modify thecontent in the value fields corresponding to any of the Dimensions ofthe Prime Data Element, in order to construct the Derivative Element.Given a candidate element, during lookup of the Sieve, if the candidateelement has different content in any of the Dimensions compared to thecorresponding Dimensions of the target Prime Data Element, a fresh PrimeData Element needs to be installed, rather than accept the derivative.For example, if a subset of the Primary Dimensions sufficiently sort theelements into distinct groups in the tree so that a candidate elementarrives at a leaf node to find a Prime Data Element that has the samecontent in this subset of Primary Dimensions but different content ineither the remaining Primary Dimensions or the Secondary Dimensions,then, instead of creating a derivative, a fresh Prime Data Element needsto be installed. This feature ensures that all data can be searchedusing the Dimensions by simply querying the Prime Data Sieve.

The Deriver may employ a variety of implementation techniques to enforcethe constraint that the Candidate Element and the Prime Data Elementmust have the exact same content in the value fields for each of thecorresponding Dimensions. The Deriver may extract information comprisingthe locations, lengths and content of the fields corresponding to theDimensions from the Skeletal Data Structure of the Prime Data Element.Similarly, this information is received from the Parser/Factorizer orcomputed for the Candidate Element. Next the corresponding fields forthe Dimensions from the candidate Element and the Prime Data Element canbe compared for equality. Once confirmed to be equal, the Deriver mayproceed with the rest of the Derivation. If there is no equality, theCandidate Element is installed in the Sieve as a fresh Prime DataElement.

The restrictions described above are not expected to significantlyhamper the degree of data reduction for most usage models. For example,if input data is comprised of a set of Elements which are data warehousetransactions of size 1000 bytes each, and if a set of 6 PrimaryDimensions and 14 Secondary Dimensions are specified by the schema, eachwith say 8 bytes of data per Dimension, the total bytes occupied bycontent at the Dimensions is 160 bytes. No perturbations are allowed onthese 160 bytes when creating a derivative. This would still leave theremaining 840 bytes of candidate element data available for perturbationto create derivatives, thus leaving ample opportunity for exploitationof redundancy, while simultaneously enabling the data from the datawarehouse to be searched and retrieved in a content associative mannerusing the Dimensions.

To execute a search query for data containing specific values for fieldsin the Dimensions, the apparatus can traverse the tree and reach a nodein the tree that matches the Dimensions specified, and all Leaf NodeData structures below that node can be returned as the result of thelookup. References to Prime Data Elements present at the Leaf Node canbe used to fetch the desired Prime Data Elements if required. TheReverse Links enable retrieval of the input Element (in losslesslyreduced format) from the Distilled File, if so desired. The Element cansubsequently be reconstituted to yield the original input data. Thus,the enhanced apparatus allows all the searching to be done on data inthe Prime Data Sieve (which is a smaller subset of the total data) whileyet being able to reach and retrieve all derivative elements as needed.

The apparatus as enhanced can be used to execute search and lookupQueries for powerful searches and retrieval of relevant subsets of databased upon the content in Dimensions specified by the query. A ContentAssociative Data Retrieval Query will have the form “Fetch (Dimension 1,value of Dimension 1; Dimension 2, Value of Dimension 2; . . . ). TheQuery will specify the Dimensions involved in the search as well as thevalues to be used for each of the specified Dimensions for contentassociative search and lookup. A query may specify all the Dimensions orit may specify only a subset of the Dimensions. The Queries may specifycompound conditions based on multiple dimensions as the criteria for thesearch and retrieval. All data in the Sieve which has the specifiedvalues for the specified Dimensions will be retrieved.

A variety of Fetch queries can be supported and made available to theAnalytics Application that is using this Content Associative DataRetrieval Apparatus. Such queries will be furnished to the apparatusthrough an interface from the application. The interface providesqueries from the application to the apparatus and returns results ofqueries from the apparatus to the application. Firstly, a queryFetchRefs can be used to fetch a reference or Handle to the Leaf NodeData Structure in FIG. 13 (along with the Child ID or index of theentry) for each Prime Data Element that matches the query. A second formof query FetchMetaData can be used to fetch the metadata (including theSkeletal Data Structure, information on the Dimensions, and Referencesto Prime Data Elements) from the entry in the Leaf Node Data Structurein FIG. 13 for each Prime Data Element that matches the query. A thirdform of query FetchPDEs will fetch all the Prime Data Elements thatmatch the search criteria. Another form of query FetchDistilledElementswill fetch all Elements in the Distilled File that match the searchcriteria. Yet another form of query FetchElements will fetch allElements in the Input Data that match the search criteria. Note that forthe FetchElements query, the apparatus will first fetch DistilledElements and then reconstitute the relevant Distilled Elements intoElements from the Input Data and return these as the results of thequery.

In addition to such multidimensional content associative Fetchprimitives, the interface may also provide to the application thecapability to directly access Prime Data Elements (using the Referenceto the Prime Data Element) and Elements in the Distilled File (using theReverse Reference to the Element). Additionally, the interface mayprovide to the application the capability to Reconstitute a DistilledElement in the Distilled File (given a Reference to the DistilledElement) and deliver the Element as it existed in the Input Data.

A judicious combination of these queries can be used by an Analyticsapplication to perform searches, determine relevant unions andintersections, and glean important insights.

FIG. 14B explained below illustrates an example of an input dataset withstructure described in structure description 1402. In this example, theinput data contained in File 1405 contains e-commerce transactions. Theinput data is converted into a series of candidate elements 1406 by theparser in the data distillation apparatus, using the schema andDimension declarations in FIG. 14A. Note how the leading bytes of theName of each candidate element are comprised of content from theDimensions. For example, the leading bytes for Name 1407 for CandidateElement 1 is PRINRACQNYCFEB. These Names are used to organize thecandidate elements in tree form. After data reduction is complete, thedistilled data is placed in Distilled File 1408.

FIG. 14C explained below illustrates how Dimension mapping description1404 can be used to parse the input dataset illustrated in FIG. 14Aaccording to structure description 1402, determine Dimensions accordingto dimension mapping description 1404, and organize Prime Data Elementsin a tree based on the determined Dimensions. In FIG. 14C, Prime DataElements are organized in a Master Tree using a total of 14 characterstaken from 4 Dimensions. Shown in the Master Tree is a portion of theLeaf Node Data Structure for the various Prime Data Elements. Note thatfor purposes of easy viewing, the complete Leaf Node Data structure ofFIG. 13 is not shown. However, FIG. 14C shows the Path Info or name ofeach entry in the leaf node data structure, the Child ID, all ReverseReferences or Reverse Links from Prime Data Elements to Elements in theDistilled File along with indicator of whether the Element in theDistilled File is “prime” (denoted by P) or “deriv” (denoted by D), andalso the Reference to the Prime Data Element. FIG. 14C shows 7 elementsin the Distilled File mapped to 5 Prime Data Elements in the MasterTree. In FIG. 14C, Reverse Link A for Prime Data Element with NamePRINRACQNYCFEB refers back to Element 1 in the Distilled File.Meanwhile, Prime Data Element with name NIKESHOELAHJUN has 3 ReverseLinks B, C, and E to Element 2, Element 3, and Element 58 reply. Notethat Element 3 and Element 58 are derivatives of Element 2.

FIG. 14D shows an auxiliary index or auxiliary tree created from theDimensions to improve the efficiency of searches. In this example theauxiliary mapping tree created is based on Dimension 2 (which isCATEGORY). By directly traversing this auxiliary tree, all elements of agiven CATEGORY in the input data can be found without more expensivetraversals of the master tree that might otherwise have been incurred.For example, a traversal down the leg that is denoted by “SHOE” leadsdirectly to two Prime Data Elements for shoes which are ADIDSHOESJCSEPand NIKESHOELAHJUN.

Alternatively, such an auxiliary tree may be based on SecondaryDimensions and used to aid in rapid convergence of searches using theDimensions.

Examples of Queries executed on the apparatus shown in FIG. 14D will nowbe provided. The Query FetchPDEs (Dimension 1, NIKE;) will return twoPrime Data Elements named NIKESHOELAHJUN and NIKEJERSLAHOCT. The QueryFetchDistilledElements (Dimension 1, NIKE;) will return Element 2,Element 3, Element 58, and Element 59 which will be Distilled Elementsin the losslessly reduced format. The Query FetchElements (Dimension 1,NIKE; Dimension 2, SHOE) will return Transaction 2, Transaction 3, andTransaction 58 from the input data File 1405. The Query FetchMetadata(Dimension 2, SHOES) will return the metadata stored in the leaf nodedata structure entry for each of the two Prime data Elements namedADIDSHOESJCSEP and NIKESHOELAHJUN.

The apparatus described thus far can be used to support searches basedupon content that is specified in fields called Dimensions.Additionally, the apparatus can be used to support searches based upon alisting of keywords that are not included in the listing of Dimensions.Such keywords may be provided to the apparatus by an application such asa search engine that is driving the apparatus. The keywords may bespecified to the apparatus via a schema declaration or passed in via akeyword list containing all the keywords, each separated by a declaredseparator (such as spaces, or commas, or linefeeds). Alternatively, botha schema as well as a keyword list may be used to collectively specifyall the keywords. A very large number of keywords may be specified—theapparatus places no limit on the number of keywords. These searchkeywords will be referred to as Keywords. The apparatus can maintain aninverted index for search using these Keywords. The inverted indexcontains for each Keyword a listing of Reverse References to Elements inthe Distilled Files that contain this Keyword.

Based upon the Keyword declarations in the schema or the Keyword list,the Parser of the Distillation Apparatus can parse the content of acandidate element to detect and locate the various Keywords (if andwhere found) in the incoming candidate element. Subsequently, thecandidate element is converted into either a Prime Data Element orDerivative Element by the Data Distillation Apparatus and placed as anElement in the Distilled File. The inverted index for the Keywords thatwere found in this Element can be updated with Reverse References tothis Element in the Distilled File. For each keyword found in theElement, the inverted index is updated to include a Reverse Reference tothis Element in the Distilled File. Recall that Elements in theDistilled File are in the losslessly reduced representation.

Upon a search Query of the data using a Keyword, the inverted index isconsulted to find and extract Reverse References to Elements in theDistilled File that contain this Keyword. Using the Reverse Reference tosuch an Element, the losslessly reduced representation of the Elementcan be retrieved, and the Element can be reconstituted. TheReconstituted Element can then be provided as the result of the searchQuery.

The inverted index can be enhanced to contain information which locatesthe offset of the Keyword in the Reconstituted Element. Note that theoffset or location of each Keyword detected in the candidate element canbe determined by the Parser and hence this information can also berecorded in the inverted index when the Reverse Reference to the Elementin the Distilled File is placed into the inverted index. Upon a searchQuery, after the inverted index is consulted to retrieve a ReverseReference to an Element in the Distilled File that contains the relevantKeyword, and after the Element is reconstituted, the recorded offset orlocation of the Keyword in the Reconstituted Element (same as theoriginal input candidate element) can be used to pinpoint where theKeyword exists in the Input data or Input File.

FIG. 15 illustrates the inverted index to facilitate search based onKeywords. For each Keyword, the inverted index contains pairs ofvalues—the first is a Reverse Reference to the losslessly reducedElement in the Distilled File that contains the Keyword, and the secondvalue is the Offset of the Keyword in the Reconstituted Element.

Dimensions and Keywords have different implications to the Prime DataSieve in the Data Distillation Apparatus. Note that the Dimensions areused as the principal axes along which to organize Prime Data Elementsin the Sieve. Dimensions form the Skeletal Data Structure of eachElement in the data. The Dimensions are declared based upon knowledge ofthe structure of the incoming data. The Deriver is constrained such thatany Derivative Element that is created must have the exact same contentas the Prime Data Element in the values of the fields for each of thecorresponding Dimensions.

These properties need not hold for the Keywords. In some embodiments,neither is there an a priori requirement that the Keywords even exist inthe data, nor is the Prime Data Sieve required to be organized based onthe Keywords, and nor is the Deriver constrained with regards toderivations involving content containing the Keywords. The Deriver isfree to create a derivative from a Prime Data Element by modifying thevalues of Keywords if necessary. The locations of the Keywords aresimply recorded where found upon scanning the input data, and theinverted index is updated. Upon a content associative search based onthe Keywords, the inverted index is queried and all locations of theKeywords are obtained.

In other embodiments, the Keywords are not required to exist in the data(the absence of Keywords in the data does not invalidate the data), butthe Prime Data Sieve is required to contain all Elements that containKeywords, and the Deriver is constrained with regards to derivationsinvolving content containing the Keywords—no derivations are allowedother than reducing duplicates. The purpose of these embodiments is thatall distinct Elements containing any Keyword must exist in the PrimeData Sieve. This is an example where the rules governing the selectionof Prime Data are conditioned by the Keywords. In these embodiments, amodified inverted index may be created which contains, for each Keyword,a Reverse Reference to each Prime Data Element containing the Keyword.On these embodiments, powerful Keyword-based search capability isrealized, wherein searching only the Prime Data Sieve is as effective assearching the entire data.

Other embodiments may exist where the Deriver is constrained so that theReconstitution Program is not allowed to perturb or modify the contentsof any Keyword found in the Prime Data Element, in order to formulate aCandidate Element as a Derivative Element of that Prime Data Element.The Keyword needs to propagate unchanged from the Prime Data Element tothe Derivative. If the Deriver needs to modify bytes of any Keywordfound in the Prime Data Element in order to successfully formulate thecandidate as a derivative of this Prime Data Element, the Derivative maynot be accepted, and the candidate must be installed as a fresh PrimeData Element in the Sieve.

The Deriver may be constrained in a variety of ways with regards toderivations involving the Keywords so that the rules governing theselection of Prime Data are conditioned by the Keywords.

The apparatus for Search of data using Keywords can accept updates tothe listing of Keywords. Keywords can be added without any changes tothe data that is stored in losslessly reduced form. When new Keywordsare added, fresh incoming data can be parsed against the updated Keywordlist, and the inverted index updated with the incoming data subsequentlybeing stored in losslessly reduced form. If the existing data (that isalready stored in losslessly reduced form) needs to be indexed againstthe new Keywords, the apparatus can progressively read in the DistilledFiles (either one or more Distilled Files at a time, or one LosslesslyReduced Data Lot at a time), reconstitute the original files (butwithout disturbing the losslessly reduced stored data), and parse thereconstituted files to update the inverted index. All this while, theentire data repository can continue to remain stored in losslesslyreduced form.

FIG. 16A illustrates a schema declaration that is a variation of theschema shown in FIG. 14A. The schema in FIG. 16A includes a declarationof a Secondary Dimension 1609 and a listing of Keywords 1610. FIG. 16Billustrates an example of an input dataset 1611 with structure describedin structure description 1602, which is parsed and converted into a setof candidate elements with names based on the declared PrimaryDimensions. The candidate elements are converted into Elements inDistilled File 1613. The declaration of the Secondary Dimension“PROD_ID” places a constraint on the Deriver such that candidate element58 may not be derived from the Prime Data Element “NIKESHOELAHJUN withPROD_ID=348”, and hence one additional Prime Data Element“NIKESHOELAHJUN with PROD_ID=349” is created in the Prime Data Sieve.Although the input dataset is the same as that shown in FIG. 14B, theoutcome of the distillation is to yield 7 Distilled Elements but 6 PrimeData Elements. FIG. 16C shows the Distilled File, the Master Tree, andthe Prime Data Elements created as a result of the distillation process.

FIG. 16D illustrates an auxiliary tree created for the SecondaryDimension “PROD_ID”. Traversing this tree with a specific PROD_ID valueleads a Prime Data Elements with that particular PROD_ID. For examplethe Query FetchPDEs (Dimension 5, 251), or alternatively the QueryFetchPDEs (PROD_ID, 251), which asks for Prime Data Elements withPROD_ID=251 yields the Prime Data Element WILSBALLLAHNOV.

FIG. 16E illustrates an inverted index (labelled Inverted Index ForKeywords 1631) created for the 3 Keywords declared in FIG. 16A Structure1610. These Keywords are FEDERER, LAVER, and SHARAPOVA. The invertedindex is updated after parsing and consuming the input dataset 1611. TheQuery FetchDistilledElements (Keyword, Federer) will utilize theinverted index (rather than the Master Tree or Auxiliary Tree) to returnElement 2, Element 3, and Element 58.

FIG. 17 shows a block diagram of the overall apparatus as enhanced forContent Associative Data Retrieval. Content Associative Data RetrievalEngine 1701 provides the Data Distillation apparatus with Schema 1704 orstructure definitions including Dimensions for the data. It alsoprovides the apparatus with Keyword lists 1705. It issues Queries 1702for search and retrieval of data from the Distillation Apparatus, andreceives the results of the queries as Results 1703. Deriver 110 isenhanced to be aware of the declarations of the Dimensions to prohibitmodification of content at the locations of the Dimensions when creatinga derivative. Note that the Reverse References from entries in the leafnode data structure to Elements in the Distilled Files are stored in theleaf node data structures in Prime Data Sieve 106. Likewise, auxiliaryindexes are also stored in Prime Data Sieve 106. Also shown is InvertedIndex 1707 which is updated with Reverse Reference 1709 by Deriver 110when the Element is being written to the Distilled Data. This ContentAssociative Data Retrieval Engine interacts with other Applications(such as Analytics, Data Warehousing, and Data Analysis Applications),providing them with results of executed Queries.

In summary, the enhanced Data Distillation apparatus enables powerfulmultidimensional content associative search and retrieval on data thatis stored in losslessly reduced form.

The Data Distillation™ apparatus can be employed for the purposes oflossless reduction of audio and video data. The data reductionaccomplished by the method is achieved by deriving components of theaudio and video data from prime data elements resident in a contentassociative sieve. Applications of the method for such purposes will nowbe described.

FIGS. 18A-B show a block diagram for an Encoder and Decoder forcompression and decompression of audio data according to the MPEG 1,Layer 3 Standard (also referred to as MP3). MP3 is an audio codingformat for digital audio which uses a combination of lossy and losslessdata reduction techniques to compress incoming audio. It manages tocompress Compact Disc (CD) audio down from 1.4 Mbps to 128 Kbps. MP3takes advantage of the limitations of the human ear to suppresscomponents of the audio that will not be perceptible to the human ear ofmost people. To achieve this, a set of techniques collectively referredto as Perceptual Coding techniques are employed, which lossily butimperceptibly reduce the size of a snippet of audio data. The PerceptualCoding techniques are lossy, and information lost during these stepscannot be regained. These Perceptual Coding techniques are supplementedby Huffman Coding, which is a lossless data reduction techniquedescribed earlier in this document.

In MP3, the incoming audio stream is compressed into a sequence ofseveral small data frames, each containing a frame header and compressedaudio data. The original audio stream is periodically sampled to producea sequence of snippets of audio which are then compressed employingPerceptual Coding and Huffman Coding to produce a sequence of MP3 dataframes. Both the Perceptual Coding and Huffman Coding techniques areapplied locally within each snippet of the audio data. The HuffmanCoding technique exploits redundancy locally within a snippet of audiobut not globally across the audio stream. Thus the MP3 techniques do notexploit redundancy globally—neither across a single audio stream, norbetween multiple audio streams. This represents an opportunity forfurther data reduction beyond what MP3 can achieve.

Each MP3 data frame represents an audio snippet of 26 ms. Each framestores 1152 samples and is subdivided into two granules each containing576 samples. As can be seen in the Encoder Block Diagram in FIG. 18A,during encoding of a digital audio signal, time domain samples are takenand converted into 576 frequency domain samples through a process offiltering and by application of the Modified Discrete Cosine Transform(MDCT). Perceptual Coding techniques are applied to reduce the amount ofinformation contained in the samples. The output of the PerceptualCoding is a Non-uniformly Quantized Granule 1810 which contains reducedinformation per frequency line. Huffman Coding is then used to furtherreduce the size of the granules. The 576 frequency lines of each granulemay use multiple Huffman tables for their encoding. The output of theHuffman Encoding is the main Data component of the frame comprisingscale factors, Huffman encoded bits, and ancillary data. Sideinformation (used to characterize and locate various fields) is placedinto the MP3 Header. The output of the Encoding is an MP3 encoded audiosignal. At a BitRate of 128 Kbps, the size of an MP3 frame is 417 or 418bytes.

FIG. 18C shows how the Data Distillation apparatus first shown in FIG.1A can be enhanced to perform data reduction on MP3 data. The methodillustrated in FIG. 18C factorizes the MP3 data into candidate elementsand exploits redundancy between elements at a grain finer than theelement itself. For MP3 data, the Granule is chosen as the Element. Inone embodiment, the Non-uniformly Quantized Granule 1810 (as shown inFIG. 18A) may be treated as the Element. In another embodiment theElement may be comprised of a concatenation of the Quantized FrequencyLines 1854 and the ScaleFactors 1855.

In FIG. 18C, the Stream of MP3 Encoded Data 1862 is received by the DataDistillation Apparatus 1863 and reduced into a stream of Distilled MP3Data 1868, stored in the losslessly reduced form. The incoming Stream ofMP3 Encoded Data 1862 comprises of a sequence of pairs of MP3 Header andMP3 Data. The MP3 Data includes CRC, Side Information, Main Data andAncillary Data. The outgoing Distilled MP3 Data created by the apparatuscomprises of a similar sequence of pairs (each pair being a DistMP3Header followed by an Element Specification in losslessly reducedformat). The DistMP3 Header contains all the components of the originalframe other than the Main Data, namely it contains the MP3 Header, CRC,Side Information, and Ancillary Data. The Element field in thisDistilled MP3 Data contains Granules specified in losslessly reducedform. Parser/Factorizer 1864 performs a first decoding of the incomingMP3 Encoded Stream (including performing Huffman decoding) to extractthe Quantized Frequency Lines 1851 and ScaleFactors 1852 (which areshown in FIG. 18B) and to generate Audio Granule 1865 as a CandidateElement. The first decoding steps performed by the Parser/Factorizer arethe same as the steps of Synchronization and Error Checking 1851,Huffman Decoding 1852, and Scale Factor Decoding 1853 of FIG. 18B—thesesteps are performed in any standard MP3 Decoder and are well known inthe existing art. Prime Data Sieve 1866 contains Granules as Prime DataElements, organized to be accessed in a Content Associative manner.During installation of a Granule into the Prime Data Sieve, the contentof the Granule is used to ascertain where in the Sieve the Granuleshould be installed and to update the Skeletal Data Structure andmetadata in the appropriate leaf node of the Sieve. Subsequently, theGranule is Huffman Coded and compressed so that it can be stored in theSieve with a footprint no greater than the footprint it occupied whenresiding in the MP3 Data. Whenever a Granule in the Sieve is needed as aPrime Data Element by the Deriver, the Granule is decompressed before itis furnished to the Deriver. Using the Data Distillation Apparatus,incoming Audio Granules are derived by Deriver 1870 from Prime DataElements (which are also Audio Granules) resident in the Sieve, and alosslessly reduced representation or distilled representation of theGranule is created and placed in the Distilled MP3 Data 1868. Thisdistilled representation of the Granule is placed into the Element fieldreplacing the Huffman Coded information that originally existed in theMain Data field of the MP3 frame. The distilled representation of eachElement or Granule is encoded using a format shown in FIG. 1H—eachElement in the Distilled Data is either a Prime Data Element(accompanied by a Reference to a Prime Data Element or Prime Granule inthe Sieve), or a Derivative Element (accompanied by a Reference to aPrime Data Element or Prime Granule in the Sieve, plus a ReconstitutionProgram that generates the Derivative Element from the Prime DataElement being referred to). During the derivation step, the Thresholdfor accepting the derivation may be set to be a fraction of the size ofthe original Huffman Coded information that resided in the Main Datafield of the frame being reduced. Thus, unless the sum of theReconstitution Program and the reference to the Prime Data Element isless than this fraction of the size of the corresponding Main Data fieldof the MP3 encoded frame (that contained Huffman coded data), thederivation will not be accepted. If the sum of the ReconstitutionProgram and the reference to the Prime Data element is less than thisfraction of the size of the existing Main Data field of the encoded MP3frame (that contained Huffman coded data), a decision can be made toaccept the Derivation.

The above described method enables the exploitation of redundancy at aglobal scope, across multiple Audio Granules stored in the apparatus.MP3 Encoded Data files may be transformed into Distilled MP3 Data andstored in losslessly reduced form. When needed to be retrieved, the dataretrieval process (employing Retriever 1871 and Reconstitutor 1872) canbe invoked to reconstitute the MP3 Encoded Data 1873. In the apparatusshown in FIG. 18C, the Reconstitutor is responsible for executing theReconstitution Program to generate the desired Granule. It isadditionally enhanced to perform the Huffman Coding step (shown asHuffman Coding 1811 in FIG. 18A) needed to generate the MP3 Encodeddata. This data can then be fed to a standard MP3 Decoder to play theaudio.

In this fashion, the Data Distillation Apparatus may be adapted andemployed to further reduce the size of MP3 audio files.

In another variation of the scheme described, upon receiving an MP3Encoded Stream, the Parser/Factorizer takes the entire Main Data fieldas a Candidate Element for derivation or as a Prime Data Element forinstallation into the Prime Data Sieve. In this variation, all Elementswill continue to remain Huffman Coded, and Reconstitution Programs willoperate upon Elements that are already Huffman Coded. This variation ofthe Data Distillation Apparatus may also be employed to further reducethe size of MP3 audio files.

The above description is presented to enable any person skilled in theart to make and use the embodiments. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein are applicable to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present invention is not limited tothe embodiments shown, but is to be accorded the widest scope consistentwith the principles and features disclosed herein.

The data structures and code described in this disclosure can bepartially or fully stored on a computer-readable storage medium and/or ahardware module and/or hardware apparatus. A computer-readable storagemedium includes, but is not limited to, volatile memory, non-volatilememory, magnetic and optical storage devices such as disk drives,magnetic tape, CDs (compact discs), DVDs (digital versatile discs ordigital video discs), or other media, now known or later developed, thatare capable of storing code and/or data. Hardware modules or apparatusesdescribed in this disclosure include, but are not limited to,application-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), dedicated or shared processors, and/or otherhardware modules or apparatuses now known or later developed.

The methods and processes described in this disclosure can be partiallyor fully embodied as code and/or data stored in a computer-readablestorage medium or device, so that when a computer system reads andexecutes the code and/or data, the computer system performs theassociated methods and processes. The methods and processes can also bepartially or fully embodied in hardware modules or apparatuses, so thatwhen the hardware modules or apparatuses are activated, they perform theassociated methods and processes. Note that the methods and processescan be embodied using a combination of code, data, and hardware modulesor apparatuses.

The foregoing descriptions of embodiments of the present invention havebeen presented only for purposes of illustration and description. Theyare not intended to be exhaustive or to limit the present invention tothe forms disclosed. Accordingly, many modifications and variations willbe apparent to practitioners skilled in the art. Additionally, the abovedisclosure is not intended to limit the present invention.

What is claimed is:
 1. A method for losslessly reducing data in a blockprocessing storage system that stores data blocks, the methodcomprising: losslessly reducing a new data block associated with alogical block address by performing a set of operations comprising:identifying one or more prime data element blocks by using the new datablock to perform a content-associative lookup on a data structure thatorganizes prime data element blocks based on their contents, and usingthe one or more prime data element blocks to losslessly reduce the newdata block, thereby obtaining a losslessly reduced representation of thenew data block; storing the losslessly reduced representation of the newdata block; and storing an association between the logical block addressand the losslessly reduced representation of the new data block.
 2. Themethod of claim 1, wherein the data structure is a tree data structure,wherein performing the content-associative lookup comprises traversing asequence of edges in the tree data structure, wherein each edge in thesequence of edges corresponds to a portion of a name associated with thenew data block, and wherein each portion of the name that is used tonavigate to a node in the tree data structure corresponds to contentthat is present in all prime data element blocks that are within asubtree rooted at the node.
 3. The method of claim 1, wherein the methodfurther comprises, incrementing a prime-data-element-count fieldassociated with each prime data element block referenced by thelosslessly reduced representation of the new data block.
 4. The methodof claim 1, wherein the association between the logical block addressand the losslessly reduced representation of the new data block replacesan association between the logical block address and a losslesslyreduced representation of an old data block, and wherein the methodfurther comprises decrementing a prime-data-element-count fieldassociated with each prime data element block referenced by thelosslessly reduced representation of the old data block.
 5. The methodof claim 1, wherein the losslessly reduced representation of the newdata block includes a reference to each prime data element block in theone or more prime data element blocks and a description of areconstitution program, wherein the new data block can be reconstitutedby applying the reconstitution program to the one or more prime dataelement blocks.
 6. The method of claim 5, wherein the method furthercomprises incrementing a reconstitution-program-count field associatedwith the reconstitution program.
 7. The method of claim 5, wherein theassociation between the logical block address and the losslessly reducedrepresentation of the new data block replaces an association between thelogical block address and a losslessly reduced representation of an olddata block, and wherein the method further comprises decrementing areconstitution-program-count field associated with a reconstitutionprogram referenced by the losslessly reduced representation of the olddata block.
 8. The method of claim 5, wherein the description of thereconstitution program specifies a sequence of transformations which,when applied to the one or more prime data element blocks, results inthe new data block.
 9. A non-transitory computer-readable storage mediumcomprising stored instructions, which when executed by a processor,cause the processor to: losslessly reduce a new data block associatedwith a logical block address by performing a set of operationscomprising: identifying one or more prime data element blocks by usingthe new data block to perform a content-associative lookup on a datastructure that organizes prime data element blocks based on theircontents, and using the one or more prime data element blocks tolosslessly reduce the new data block, thereby obtaining a losslesslyreduced representation of the new data block; store the losslesslyreduced representation of the new data block; and store an associationbetween the logical block address and the losslessly reducedrepresentation of the new data block.
 10. The non-transitorycomputer-readable storage medium of claim 9, wherein the data structureis a tree data structure, wherein performing the content-associativelookup comprises traversing a sequence of edges in the tree datastructure, wherein each edge in the sequence of edges corresponds to aportion of a name associated with the new data block, and wherein eachportion of the name that is used to navigate to a node in the tree datastructure corresponds to content that is present in all prime dataelement blocks that are within a subtree rooted at the node.
 11. Thenon-transitory computer-readable storage medium of claim 9, furthercomprising stored instructions, which when executed by the processor,cause the processor to increment a prime-data-element-count fieldassociated with each prime data element block referenced by thelosslessly reduced representation of the new data block.
 12. Thenon-transitory computer-readable storage medium of claim 9, wherein theassociation between the logical block address and the losslessly reducedrepresentation of the new data block replaces an association between thelogical block address and a losslessly reduced representation of an olddata block, and wherein the method further comprises decrementing aprime-data-element-count field associated with each prime data elementblock referenced by the losslessly reduced representation of the olddata block.
 13. The non-transitory computer-readable storage medium ofclaim 9, wherein the losslessly reduced representation of the new datablock includes a reference to each prime data element block in the oneor more prime data element blocks and a description of a reconstitutionprogram, wherein the new data block can be reconstituted by applying thereconstitution program to the one or more prime data element blocks. 14.The non-transitory computer-readable storage medium of claim 13, whereinthe method further comprises incrementing a reconstitution-program-countfield associated with the reconstitution program.
 15. The non-transitorycomputer-readable storage medium of claim 13, wherein the associationbetween the logical block address and the losslessly reducedrepresentation of the new data block replaces an association between thelogical block address and a losslessly reduced representation of an olddata block, and wherein the method further comprises decrementing areconstitution-program-count field associated with a reconstitutionprogram referenced by the losslessly reduced representation of the olddata block.
 16. The non-transitory computer-readable storage medium ofclaim 13, wherein the description of the reconstitution programspecifies a sequence of transformations which, when applied to the oneor more prime data element blocks, results in the new data block.
 17. Asystem comprising: a memory storing instructions; and a processor,coupled with the memory and to execute the instructions, theinstructions when executed cause the processor to: losslessly reduce anew data block associated with a logical block address by performing aset of operations comprising: identifying one or more prime data elementblocks by using the new data block to perform a content-associativelookup on a data structure that organizes prime data element blocksbased on their contents, and using the one or more prime data elementblocks to losslessly reduce the new data block, thereby obtaining alosslessly reduced representation of the new data block; store thelosslessly reduced representation of the new data block; and store anassociation between the logical block address and the losslessly reducedrepresentation of the new data block.
 18. The system of claim 17,wherein the data structure is a tree data structure, wherein performingthe content-associative lookup comprises traversing a sequence of edgesin the tree data structure, wherein each edge in the sequence of edgescorresponds to a portion of a name associated with the new data block,and wherein each portion of the name that is used to navigate to a nodein the tree data structure corresponds to content that is present in allprime data element blocks that are within a subtree rooted at the node.19. The system of claim 17, wherein the instructions, which whenexecuted by the processor, cause the processor to increment aprime-data-element-count field associated with each prime data elementblock referenced by the losslessly reduced representation of the newdata block.
 20. The system of claim 17, wherein the association betweenthe logical block address and the losslessly reduced representation ofthe new data block replaces an association between the logical blockaddress and a losslessly reduced representation of an old data block,and wherein the instructions, which when executed by the processor,cause the processor to decrement a prime-data-element-count fieldassociated with each prime data element block referenced by thelosslessly reduced representation of the old data block.